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Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches

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Land development in sub-urban areas is more frequent than in highly urbanized cities, causing land prices to increase abruptly and making it harder for valuers to update land values in timely manner. Apart from this, the non-availability of sufficient reliable market values forces valuers to use alternatives and subjective judgement. Land value is critical not only for private individuals but also for government agencies in their day-to-day land dealings. Thus, mass appraisal is necessary. In other words, despite the importance of reliable land value in all aspects of land administration, valuation remains disorganized, with unregulated undertakings that lack concrete scientific, legal, and practical foundations. A holistic and objective way of weighing geospatial factors through expert consultation, legal reviews, and evidence (i.e., news) will provide more realistic results than a regression-based method that does not comprehend valuation factors (i.e., physical, social, economic, environmental, and legal aspects). The analytic hierarchy process (AHP) enables these factors to be included in the model, hence providing a realistic result. The innovative land valuation model (iLVM), developed in this study, is an inclusive approach wherein experts are involved in the selection and weighing of 15 factors through the AHP. The model was validated using root mean squared error (RMSE) and compared with multiple regression analysis (MRA) through a case study in Baybay City, Philippines. Based on the results, the iLVM (RMSE = 0.526) outperformed MRA (RMSE = 1.953).
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sustainability
Article
Development of an Innovative Land Valuation Model
(iLVM) for Mass Appraisal Application in Sub-Urban
Areas Using AHP: An Integration of Theoretical and
Practical Approaches
Jannet C. Bencure 1, *, Nitin K. Tripathi 1, Hiroyuki Miyazaki 1, Sarawut Ninsawat 1
and Sohee Minsun Kim 2
1Remote Sensing and Geographic Information System, School of Engineering Technology,
Asian Institute of Technology, 58 Moo 9, Klong Luang, Pathumthani 12120, Thailand
2Department of Development and Sustainability, School of Environment, Resources and Development,
Asian Institute of Technology, 58 Moo 9, Klong Luang, Pathumthani 12120, Thailand
*Correspondence: jcbencure@gmail.com
Received: 10 June 2019; Accepted: 4 July 2019; Published: 8 July 2019


Abstract:
Land development in sub-urban areas is more frequent than in highly urbanized cities,
causing land prices to increase abruptly and making it harder for valuers to update land values
in timely manner. Apart from this, the non-availability of sucient reliable market values forces
valuers to use alternatives and subjective judgement. Land value is critical not only for private
individuals but also for government agencies in their day-to-day land dealings. Thus, mass appraisal
is necessary. In other words, despite the importance of reliable land value in all aspects of land
administration, valuation remains disorganized, with unregulated undertakings that lack concrete
scientific, legal, and practical foundations. A holistic and objective way of weighing geospatial factors
through expert consultation, legal reviews, and evidence (i.e., news) will provide more realistic results
than a regression-based method that does not comprehend valuation factors (i.e., physical, social,
economic, environmental, and legal aspects). The analytic hierarchy process (AHP) enables these
factors to be included in the model, hence providing a realistic result. The innovative land valuation
model (iLVM), developed in this study, is an inclusive approach wherein experts are involved in the
selection and weighing of 15 factors through the AHP. The model was validated using root mean
squared error (RMSE) and compared with multiple regression analysis (MRA) through a case study
in Baybay City, Philippines. Based on the results, the iLVM (RMSE =0.526) outperformed MRA
(RMSE =1.953).
Keywords:
land valuation; mass appraisal; real estate; analytic hierarchy process (AHP); multiple
regression analysis (MRA); geographic information system (GIS)
1. Introduction
Fast and updated land valuation has become part of the economic agenda [
1
] recently, especially
in government land-related transactions such as taxation, expropriation, fragmentation, reallocation,
and consolidation [
2
], or even in land administration and management [
3
]. Land value is important for
people in the agricultural, financial, and business sectors since it determines their position in lending
institution in terms of borrowing capacity [4].
Land is the most precious and limited non-renewable resource [
5
]; yet, it is one of the most
exploited and undervalued natural resources [
6
]. In addition, rapid population growth and urbanization
increase demands for land; hence, it is essential for government to adopt a more meaningful and
Sustainability 2019,11, 3731; doi:10.3390/su11133731 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 3731 2 of 17
practical way for land use planning and administration to have a more sustainable community, that is,
by incorporating land value in such activity. Land use management could be easier if land were
assigned value [
1
]. The recession of the property market in the United Kingdom (1980s), Japan
(1990s), Germany (1994), and the United States (2007) provides concrete evidence on the eects of
land value and how significant it is in the land administration system [
3
]. In 2016, the 2030 agenda
for sustainable development, known as Global Goals, linked the importance of improved land use
planning, administration, and management in achieving their goals. The value of land depends on the
physical, economic, social, environmental, and legal factors [
5
]. Nevertheless, defining a precise and
perfect valuation model remains dicult due to variations of such factors [
7
] and valuers’ perceptions;
hence, value is most often perceived as inconsistent and biased. This type of situation is no big issue
for single and one-time pass land valuation. In contrast, regular and massive valuation, for example
through mass appraisal for taxation, expropriations, land administration, and other similar purposes,
requires updated and high levels of consistency and transparency.
Land valuation is the process of estimating the absolute [
8
11
] or relative value [
12
] value of land.
Regardless of the purpose or extent of the area, valuation can be done manually or automatically.
The latter involves collection of market values that serve as sample values, from which empirical
analysis and calibration is performed to derive numerical valuation model for the area. The used of
an automatic valuation model (AVM) has been popular for more than ten years in developed countries
like Sweden, Canada, and the United States, and is becoming popular worldwide [2].
The most popular AVM approaches are based on regression analysis, ranging from simple to hybrid
regression, such as multiple regression analysis [
2
], valuation method based on the two cumulative
distribution functions (VMTCDF)by Ballestero of 1971 [
13
], spatial Bayesian [
14
], geographically
weighted regression [
15
,
16
], and ridge regression [
17
], among others. Artificial intelligence (AI)
techniques like artificial neural networks [
10
], genetic algorithms [
11
,
17
], case-based reasoning [
18
],
and random forest [
19
] are becoming popular. Moreover, a factor-weighting approach like multi-criteria
decision analysis (MCDA), has been also utilized by several studies [
7
,
20
23
]. The MCDA-based
approach estimates relative land value that is most often expressed as an index (i.e., rank) with
equivalent qualitative descriptions such as high or low value. The Storie Index, a well-known and
accepted method in California of valuating agricultural land based on soil characteristics [
22
,
23
],
is an example of a factor-weighting approach. Meanwhile, few have performed interpolation techniques
such as those in the study of [24].
One of the significant limitations of regression-based and AI techniques is that they do not
comprehend the real-world valuation factors [
20
,
25
] because they are data-dependent. For example,
in an attempt to establish a relationship between land value and elevation and road proximity made
using data from areas that are all or mostly located at relatively similar elevations, elevation will
obviously appear to be more significant than road proximity when employing this technique, which is
not the real case. In contrast, MCDA enables us to select, rank, and weigh factors based on experts who
often perform value judgement; hence, the result is more realistic. Moreover, a model is holistic when
it considers both the negative and positive influence of geospatial factors (i.e., physical, environmental,
economic, social, and legal) to the land. These factors can be considered when MCDA is employed.
Another weakness of the former methods is that they require significant land value data to achieve
desirable results [
12
]. Data that involve money are most often confidential and not publicly available,
although sometimes they are available but not reliable. For example, a technical report by the
Philippines Land Administration and Management Project (2002) mentioned that sellers or buyers
incorrectly declared the selling price to avoid paying higher transfer tax. Obviously, when data are not
available, valuers may be forced to use alternatives, like adopting sales from other district and then
making an adjustment—this makes the valuation inconsistent.
Therefore, to overcome the above-mentioned limitations, the current study aimed at developing
the innovative land valuation model (iLVM) for mass appraisal applications utilizing the MCDA
technique, particularly the analytic hierarchy process (AHP) in geographic information systems (GIS),
Sustainability 2019,11, 3731 3 of 17
and generating a land value map. The geospatial factors were identified and ranked based on survey,
interview, news, existing laws and standards, and related studies. The 15 valuation factors considered
in the study are: proximity to roads (three types), schools (two types), hospitals, central business
district (CBD), industry, river/lake, coastline, active fault line, land use, slope, aspect, and elevation.
These were grouped into five main categories: physical, social, economic, legal, and environmental.
The performance of the developed iLVM was validated using root mean squared error (RMSE) and
compared with MRA through a case study of Baybay City, Philippines. The legal factors were based on
the existing laws of the Philippines (e.g., the Water Code of the Philippines and the Philippine Disaster
Risk Reduction and Management Act of 2010, among others) since the model is tested in Baybay
City. Sub-urban cities like Baybay City are perfect for the study because horizontal development is
more frequent than highly urbanized development. These infrastructure developments caused land
prices to peak more abruptly than normal, making more dicult for appraisers to update land values.
Also, Baybay City has been employing a manual method in valuation undertakings despite its cityhood
status. The advantage of this method is that it overcomes the limitations of regression-based and its
applicability at a larger scale. It is still subjective yet less biased, and is transparent and flexible enough
to be applied based on the condition of locality or country in a theoretical, logical, and realistic manner.
The paper is arranged as follows: Section 2presents the theories, framework, and methods
employed in developing the model; Section 3describes the data used, study area, and developed iLVM
wherein the model performance is implemented and validated through a case study; Section 4presents
the results; Section 5discusses the results; and Section 6presents a summary and conclusion and
pinpoints the strengths and drawbacks of the iLVM.
2. Methodology
The study is GIS-based, wherein 15 geospatial factors are considered in the development of the
iLVM. The goal is to develop an innovative land valuation model by involving the experts of real
estate such as assessors and appraisers, and government ocials concerned with land resources in the
dierent phases of development through a survey questionnaire and in-depth interview. The residents
were also asked through separate field questionnaires which factors mattered to them. Moreover,
the existing laws of the Philippines (e.g., the Water Code of the Philippines, the National Building
Code of the Philippines, and the Philippine Disaster Risk Reduction and Management Act of 2010,
among others), principles on valuation standards (i.e., International Valuation Standards, and RICS
Valuation-Global Standards), and previous literature also aided in the development process.
The analytic hierarchy process (AHP) was employed because of its accuracy, simplicity, and theoretically
robust capability for handling both numerical and non-numerical measurements [
26
], as well as its ability to
embrace real-world factors in the model. The common problem with valuation, especially in developing
countries, is limited or non-availability of land value data [
23
] that could affect MRA results. Hence, the AHP
is used in the current study instead of MRA. The spatial layers are stored in the database and processed
in GIS software, while Python scripting is used to automate geoprocessing task, thus minimizing human
intervention. The overall workflow of iLVM development is presented in Figure 1.
Sustainability 2019,11, 3731 4 of 17
Sustainability 2019, 11, x FOR PEER REVIEW 4 of 17
Figure 1. Overall workflow of the innovative land valuation model (iLVM) development.
NRoad: national road; MRoad: municipal/city road; Broad: barangay road; CBD: central
business district; Sec. Sch.: secondary school; RMSE: root mean square error; MRA: multiple
regression analysis; LV Map: land value map.
In the scenarios of the Philippines, national roads (highways) form the main land transportation
system that connects the country’s three major island and provides access to major population
centers. These roads are further complemented with provincial roads, municipal or city roads, and
barangay roads that respectively provide interconnectivity among cities and municipalities (not
traversed by national roads), within a municipality or city, and on interior barangays or villages [27].
2.1. Analytic Hierarchy Process
The analytic hierarchy process is the most popular multicriteria decision-making method that
quantitatively measures the expert’s opinion in the form of weights [26]. The AHP process initially
involves a pair-wise comparison matrix wherein the relative dominance of each factor (or sub-factor)
is compared with respect to the common variable. The consistency of derived weights (eigenvectors)
is checked by calculating consistency ratio [20].
Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many
research fields and practical applications [28]. This is because the AHP: (1) overcomes human
difficulty in making simultaneous judgment among factors to be considered in the model; (2) is
relatively simple as compared to other MCDA methods; (3) is flexible to be integrated in various
techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in
judgement [26].
2.2. Geospatial Land Valuation Factors for iLVM Development
Figure 1.
Overall workflow of the innovative land valuation model (iLVM) development. NRoad:
national road; MRoad: municipal/city road; Broad: barangay road; CBD: central business district; Sec.
Sch.: secondary school; RMSE: root mean square error; MRA: multiple regression analysis; LV Map:
land value map.
In the scenarios of the Philippines, national roads (highways) form the main land transportation
system that connects the country’s three major island and provides access to major population centers.
These roads are further complemented with provincial roads, municipal or city roads, and barangay
roads that respectively provide interconnectivity among cities and municipalities (not traversed by
national roads), within a municipality or city, and on interior barangays or villages [27].
2.1. Analytic Hierarchy Process
The analytic hierarchy process is the most popular multicriteria decision-making method that
quantitatively measures the expert’s opinion in the form of weights [
26
]. The AHP process initially
involves a pair-wise comparison matrix wherein the relative dominance of each factor (or sub-factor) is
compared with respect to the common variable. The consistency of derived weights (eigenvectors) is
checked by calculating consistency ratio [20].
Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many
research fields and practical applications [
28
]. This is because the AHP: (1) overcomes human diculty
in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple
as compared to other MCDA methods; (3) is flexible to be integrated in various techniques such as
programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgement [26].
Sustainability 2019,11, 3731 5 of 17
2.2. Geospatial Land Valuation Factors for iLVM Development
The 15 factors finally selected during survey, interview, and review of existing standards and
literature, were grouped as physical, social, economic, environmental, and legal accordingly to
a developed hierarchical structure (Figure 2). The physical factors refer to the physical attributes of
land such as elevation, slope, aspect, and land use. Environmental factors describe the susceptibility
of land to hazards such as flood (proximity to river), earthquake (nearness to the active fault zone),
air pollution (proximity to industry), and storm surge (proximity to coastline). Social factors are the
benefits for the society the land may bring due to its location relative to roads, hospitals, schools,
and rivers (amenity benefits). Economic factors are economic benefits of the land due to its nearness
to the shopping centers, factory/industry, and coastline. Legal factors are the legal constraints of the
land such as permitted land use, salvage zone and other restrictions. The coastline is categorized
as economic because of the business establishments are found along the beach area. In each factor,
the raster layer of 2-m pixel resolution is generated using the Euclidean distance tool.
Sustainability 2019, 11, x FOR PEER REVIEW 5 of 17
The 15 factors finally selected during survey, interview, and review of existing standards and
literature, were grouped as physical, social, economic, environmental, and legal accordingly to a
developed hierarchical structure (Figure 2). The physical factors refer to the physical attributes of
land such as elevation, slope, aspect, and land use. Environmental factors describe the susceptibility
of land to hazards such as flood (proximity to river), earthquake (nearness to the active fault zone),
air pollution (proximity to industry), and storm surge (proximity to coastline). Social factors are the
benefits for the society the land may bring due to its location relative to roads, hospitals, schools, and
rivers (amenity benefits). Economic factors are economic benefits of the land due to its nearness to
the shopping centers, factory/industry, and coastline. Legal factors are the legal constraints of the
land such as permitted land use, salvage zone and other restrictions. The coastline is categorized as
economic because of the business establishments are found along the beach area. In each factor, the
raster layer of 2-m pixel resolution is generated using the Euclidean distance tool.
Figure 2. Geospatial factors included in the iLVM development. CBD: central business
district
2.3. Hierarchical Modelling
After the 15 factors were identified and categorized as mentioned in the previous section,
assigning of relative weights of the first hierarchy (i.e., physical, social, economic, environmental, and
legal) was then performed (Figure 3). In a similar manner, the relative weights of the second hierarchy
(i.e., 15 subfactors) in each factor-category were also computed. In both processes, the AHP was used
to derive the relative weights of each factor-category and subfactor.
Figure 2. Geospatial factors included in the iLVM development. CBD: central business district.
2.3. Hierarchical Modelling
After the 15 factors were identified and categorized as mentioned in the previous section, assigning
of relative weights of the first hierarchy (i.e., physical, social, economic, environmental, and legal)
was then performed (Figure 3). In a similar manner, the relative weights of the second hierarchy
(i.e., 15 subfactors) in each factor-category were also computed. In both processes, the AHP was used
to derive the relative weights of each factor-category and subfactor.
Sustainability 2019,11, 3731 6 of 17
Sustainability 2019, 11, x FOR PEER REVIEW 6 of 17
Figure 3. Hierarchical structure of land valuation geospatial factors and sub-factors. F01:
national road; F02: municipal/city road; F03: barangay road; F04: CBD; F05: industry; F06:
hospitals; F07: university; F08: secondary school; F09: freshwater; F10: coastline; F11:
faultzone; F12: landuse; F13: slope; F14: elevation; F15: aspect; Cm: commercial; In:
industrial; Rs: residential; Ag: agricultural; Ot: other land uses such as forests, open space,
etc.
2.4. Scoring of Sub-Factors
Each sub-factor was classified and assigned score of 0 to 5, with 5 being highest effect on land
value and 0 meaning no effect (Table 1). The classifications (i.e., distances, elevation threshold, etc.)
are based on the expert’s advice (i.e., for economic and social factors), existing laws and standards
(i.e., legal factors), and news (i.e., for environmental factors).
Table 1. Scores of land valuation sub-factors.
Figure 3.
Hierarchical structure of land valuation geospatial factors and sub-factors. F01: national road;
F02: municipal/city road; F03: barangay road; F04: CBD; F05: industry; F06: hospitals; F07: university;
F08: secondary school; F09: freshwater; F10: coastline; F11: faultzone; F12: landuse; F13: slope; F14:
elevation; F15: aspect; Cm: commercial; In: industrial; Rs: residential; Ag: agricultural; Ot: other land
uses such as forests, open space, etc.
2.4. Scoring of Sub-Factors
Each sub-factor was classified and assigned score of 0 to 5, with 5 being highest eect on land
value and 0 meaning no eect (Table 1). The classifications (i.e., distances, elevation threshold, etc.)
are based on the expert’s advice (i.e., for economic and social factors), existing laws and standards
(i.e., legal factors), and news (i.e., for environmental factors).
Sustainability 2019,11, 3731 7 of 17
Table 1. Scores of land valuation sub-factors.
Sustainability 2019, 11, x FOR PEER REVIEW 7 of 17
1 100-m (both side) from active Faultline, based on [29] study.
2.5. Evaluation of Judgement Consistency and Validation of iLVM Performance
The consistencies of the judgement are checked if it meets the allowable limit, that is, 0.1 or less
[30]. On the other hand, the validation involves two steps: (1) conversion of weights into monetary
unit (i.e., Philippine currency), and (2) RMSE computation. The numerical values derived from AHP
are still relative index, ranging from 1 to 5. Hence, it is necessary to convert these values into
monetary terms to compare it with the market values. The transformed values, that represent the
land market value in Philippine currency (i.e., PhP), were then compared to 118 collected samples,
from which the RMSE was determined. Performance of the developed iLVM was further compared
to MRA, a well-known valuation method.
3. Data and Case Study
3.1. Study Area: Baybay City, Philippines
Philippines is one of the developing countries in Southeast Asia. The country is further
administratively divided into 17 regions, with each region composed of provinces. Each province is
divided into cities and municipalities (or towns), and municipalities into barangays. Baybay City is
the second largest city and largest town in the province of Leyte in terms of area and population. It
F01 National Road km <0.5 0.5-1.0 1.0-1.5 1.5-2.5 2.5-3.0 >3.0
F02 Mun/City Road km <0.2 0.2-0.5 0.5-1.0 1.0-2.0 2.0-2.5 >2.5
F03 Barangay Road km <0.01 0.1-0.2 0.2-0.3 0.3-0.5 0.5-1.0 >1.0
F06 Hospitals km <1.0 1.0-2.0 2.0-3.0 3.0-5.0 5.0-5.5 >5.5
F07 University km <0.5 0.5-1.0 1.0-1.5 1.5-2.0 2.0-2.5 >2.5
F08 Secondary Sch. km <0.3 0.3-0.6 0.6-0.9 0.9-1.2 1.2-1.5 >1.5
F09 Freshwater km <0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1.0 >1.0
Social
Code
Name Unit
5 4 3 2 1 0
SCORE
F04 CBD km <2.0
2.0-5.0
5-10.0
10-15.0
15-20.0
>20.0
F05 Industry km <0.5 0.5-1.0 1.0-2.0 2.0-3.0 3.0-3.5 >3.5
F10 Coastline km <0.5 0.5-1.0 1.0-1.5 1.5-2.0 2.0-2.5 >2.5
Economic
F12 Landuse - Cm In Rs Ag Ot -
F13 Slope o <6 6-9 9-12 12-18 >18 -
F14 Elevation m <50 50-100 100-200
200-300
300-500
-
F15 Aspect o
<135 - 135-225
225-315
>315 -
Physical
F05 Industry km >5.0 3.0-5.0 2.0-3.0 1.0-2.0 <1.0 -
F09 Freshwater km >0.1 0.06-0.1
0.04-0.06
0.02-0.04
<0.02 -
F10 Coastline km >0.12 0.09-0.12
0.06
-0.09
0.03-0.06
<0.03 -
F11 Faultzone1 km >30 15-30.0 10-15.0 5.0-10 <5.0 -
Environmental
F01 National Road km >0.02 - - - <0.02 -
F05 Industry km >1.0 - - - <1.0 -
F09 Freshwater km >0.03 - - - <0.03 -
F10 Coastline km >0.1 - - - <0.1 -
Legal
Land Value
1100-m (both side) from active Faultline, based on [29] study.
2.5. Evaluation of Judgement Consistency and Validation of iLVM Performance
The consistencies of the judgement are checked if it meets the allowable limit, that is, 0.1 or
less [
30
]. On the other hand, the validation involves two steps: (1) conversion of weights into monetary
unit (i.e., Philippine currency), and (2) RMSE computation. The numerical values derived from AHP
are still relative index, ranging from 1 to 5. Hence, it is necessary to convert these values into monetary
terms to compare it with the market values. The transformed values, that represent the land market
value in Philippine currency (i.e., PhP), were then compared to 118 collected samples, from which
the RMSE was determined. Performance of the developed iLVM was further compared to MRA,
a well-known valuation method.
Sustainability 2019,11, 3731 8 of 17
3. Data and Case Study
3.1. Study Area: Baybay City, Philippines
Philippines is one of the developing countries in Southeast Asia. The country is further administratively
divided into 17 regions, with each region composed of provinces. Each province is divided into cities
and municipalities (or towns), and municipalities into barangays. Baybay City is the second largest city
and largest town in the province of Leyte in terms of area and population. It is a coastal municipality
in the province, consisting of 92 barangays, 10 of which are urban barangays, and the remaining 82 are
rural. According to the 2015 census, there were 109,432 inhabitants [
31
]. Its GIS-based computed area is
40,375 hectares as per Political Survey, Land Management Bureau record, of which approximately 40% are
alienable and disposable (Figure 4). Most of the areas are described as undulating to steep slopes, while the
remaining flat areas are dominantly located in coastal areas. In terms of economy, agriculture is the common
livelihood however, large portion of the city’s revenue were derived from business establishments [32].
In the context of mass appraisal, more than 75% of the provinces and at least 80% of the cities
are still using outdated land market values [
32
]. Lack of sales data that delays the valuation process
and absence of standards are among the pinpointed reasons. In fact, most municipalities are adopting
manual valuation despite their awareness on the inconsistency and lack of transparency of such
method. Baybay City was chosen to be the case study area because of four reasons: (1) it is a sub-urban
area, (2) as a newly established city, Baybay has undergone rapid infrastructure development, making
it more dicult to update land values, (3) it is one of the 80% of cities that are still using outdated
market land values, and (4) assessors are still employing the manual valuation method.
Sustainability 2019, 11, x FOR PEER REVIEW 8 of 17
is a coastal municipality in the province, consisting of 92 barangays, 10 of which are urban barangays,
and the remaining 82 are rural. According to the 2015 census, there were 109,432 inhabitants [31]. Its
GIS-based computed area is 40,375 hectares as per Political Survey, Land Management Bureau
record, of which approximately 40% are alienable and disposable (Figure 4). Most of the areas are
described as undulating to steep slopes, while the remaining flat areas are dominantly located in
coastal areas. In terms of economy, agriculture is the common livelihood however, large portion of
the city’s revenue were derived from business establishments [32].
In the context of mass appraisal, more than 75% of the provinces and at least 80% of the cities
are still using outdated land market values [32]. Lack of sales data that delays the valuation process
and absence of standards are among the pinpointed reasons. In fact, most municipalities are adopting
manual valuation despite their awareness on the inconsistency and lack of transparency of such
method. Baybay City was chosen to be the case study area because of four reasons: (1) it is a sub-
urban area, (2) as a newly established city, Baybay has undergone rapid infrastructure development,
making it more difficult to update land values, (3) it is one of the 80% of cities that are still using
outdated market land values, and (4) assessors are still employing the manual valuation method.
(a)
(b)
Figure 4. Map of Baybay City, Philippines. (a) The location of Leyte Province and Baybay
City per DENR-LMB record. (b) The existing land use map of Baybay City, Philippines as
per Baybay City Local Government Unit, 2010.
3.2. Data and Preprocessing
Most of the secondary data were collected from Philippine government agencies (Table 2). There
are 9070 cadastral parcels, in local coordinate system. For uniformity, all spatial data were
transformed into the Philippine Reference System 1992 (PRS92) coordinate system. In addition, the
coastline was extracted from Landsat OLI using McFeeters (1996) Normalized Difference Water
Index.
The primary data and other relevant information were gathered in four phases. First, an initial
in-depth interview with the agencies involved in land valuation was conducted to aid in the
composition of survey questionnaire. Second, in-person and online discussions with real estate
appraisers, assessors, and environmentalist were also performed to seek advice on both classification
and scoring of sub-factors. Next, another survey questionnaire was prepared for residents that aimed
to supplement sales data and determine land market value in their locality. Lastly, existing laws and
Figure 4.
Map of Baybay City, Philippines. (
a
) The location of Leyte Province and Baybay City per
DENR-LMB record. (
b
) The existing land use map of Baybay City, Philippines as per Baybay City Local
Government Unit, 2010.
3.2. Data and Preprocessing
Most of the secondary data were collected from Philippine government agencies (Table 2). There are
9070 cadastral parcels, in local coordinate system. For uniformity, all spatial data were transformed
into the Philippine Reference System 1992 (PRS92) coordinate system. In addition, the coastline was
extracted from Landsat OLI using McFeeters (1996) Normalized Dierence Water Index.
Sustainability 2019,11, 3731 9 of 17
The primary data and other relevant information were gathered in four phases. First, an initial
in-depth interview with the agencies involved in land valuation was conducted to aid in the composition
of survey questionnaire. Second, in-person and online discussions with real estate appraisers, assessors,
and environmentalist were also performed to seek advice on both classification and scoring of
sub-factors. Next, another survey questionnaire was prepared for residents that aimed to supplement
sales data and determine land market value in their locality. Lastly, existing laws and standards,
newspapers, and the Internet were reviewed as well to acquire relevant information related to
environment and legal aspects.
Table 2. Description and sources of secondary data used in the study.
Data Description Sources Format
1. Landuse based on the LU Plan Local Government Unit, Baybay City Map
2. IfSAR DEM 1Dept. of Science and Technology, Philippines Raster
3. Hospital location Dept. of Health, Philippines (www.doh.gov.ph)
Google maps Shp 2
4. Road network
Dept. of Public Works and Highways, Philippines
Local Government Unit, Baybay City;
Local Government Unit, Baybay City;
Open Street Map; Google Satellite Image
Shp 2
5. Schools Commission on Higher Education, Philippines
(www.ched.gov.ph); www.gov.ph; Google maps List/Shp
6. Freshwater PhilGIS (www.philgis.org)Shp 2
7. Center Business Center Local Government Unit, Baybay City Map
8. Industries Local Government Unit, Baybay City; Map
Open Street Map, Google Satellite Image Shp 2
9. Landsat OLI8, P113/R52-53 https://earthexplorer.usgs.gov/(for coastline) Raster
10. Active Fault Line PHIVOLCS
http://faultfinder.phivolcs.dost.gov.ph/Shp 2
11. Sales data (2013–2015) Local Government Unit, Baybay City List
12. Cadastral Lot DENR-Land Management Bureau, Philippines CAD
1IfSAR DEM: Interferometric Synthetic Aperture Radar Digital Elevation Model; 2Shp—shapefile.
4. Results
4.1. Weights of Main Factors and Sub-factors
The current study identified 15 factors in the LVM development. These factors were grouped
into five categories and each category was assigned weights using the AHP method with the aid of
expert advice. Further, sub-factor of each category was weighted also with the AHP. The pair-wise
comparison and weights of the main factors (Table 3) and sub-factors (Table 4) are shown along with the
respective consistency ratio (CR) value. In all cases, CR value is less than 0.10; this implies judgements
are consistent and hence weights are acceptable.
Sustainability 2019,11, 3731 10 of 17
Table 3. Pair-wise comparison and weights of main factors.
Physical Social Economic Environment Legal Weights
Physical 1 1/3 1/2 2 4 0.184
Social 3 1 3 3 5 0.432
Economic 2 1/3 1 2 2 0.201
Environment 1/2 1/3 1/2 1 1 0.101
Legal 1/4 1/5 1/2 1 1 0.082
CR =0.06
Table 4. Pair-wise comparison and weights of sub-factors.
a. Physical factors b. Legal factors
F13 F14 F15 F12 Weights F01 F05 F09 F10 Weights
F13 1 2 3 1/3 0.237 F01 1 1 3
1/2
0.265
F14 1/2 1 2 1/3 0.151 F05 1 1 2
1
0.275
F15 1/3 1/2 1 1/6 0.080 F09
1/3 1/2
1
1/2
0.128
F12 3 3 6 1 0.532 F10 2 1 2
1
0.332
CR =0.02 CR =0.05
c. Social factors
F01 F02 F03 F06 F07 F08 F09 Weights
F01 1 2 3 1 2 1 5 0.223
F02 1/2 1 1 1/2 1/3 1 4 0.113
F03 1/3 1 1 1 2 1 4 0.145
F06 1 2 1 1 3 2 5 0.220
F07 1/2 3 1/2 1/3 1 1/2 3 0.119
F08 1 1 1 1/2 2 1 2 0.139
F09 1/5 1/4 1/4 1/5 1/3 1/2 1 0.041
CR =0.07
d. Economic factors e. Environmental factors
F04 F05 F10 Weights F05 F09 F11 F10 Weights
F04 1 3 2 0.539 F05 1 1
1/2 1
0.204
F05 1/3 1 1/2 0.164 F09 1 1
1/2 1
0.204
F10 1/2 2 1 0.297 F11 2 2 1
1
0.346
CR =0.01 F10 1 1 1
1
0.246
CR =0.02
4.2. The Developed iLVM and Its Perfomance
With the computed weights, the LVM’ general Equation (1) was derived to produce a single layer
necessary to produce a land value map. Since the numerical values computed from Equation (2) have
no physical unit except that they only show the relative land values in the study area, these values
were further converted into market values for proper validation and comparison. There were 118 items
of market data (min =1.50; max =35,000) used to evaluate the developed model. Market data that
represent land value (in the current study) were transformed into logarithmic form (base 10) to address
skewed data [
33
]. Then, a linear relationship between AHP and market values was assumed [
34
],
initially with an RMSE of 0.547. It was found out that errors occurred at extreme AHP values (i.e., upper
ends); then the model was further improved into a more complex expression presented in Equation (2)
with an improved RMSE of 0.526.
LVM0=WPXWipFip+WSXWiSFiS+WEXWiEFiE+WRXWiRFiR+WLXWiLFiL(1)
where W
P
,W
S
,W
E
,W
R
, and W
L
are the respective main weights of the physical, social, economic,
environmental, and legal factors; Wirepresents the weights of ith sub-factors Fi.
LVM =(100.5335 +1.2023LVM0;LVM02.0
100.1858 +3.012ln(LVM0);LVM0>2.0 (2)
Sustainability 2019,11, 3731 11 of 17
where LVM is the land value per square meter in the Philippine Peso (PhP). LVM’ is computed
using Equation (1).
The model is evaluated with root-mean-square error (RMSE) and compared with MRA. Figure 5shows
the visual representation of the model performance, with (a) being the fitted line plot between the actual and
predicted value, and (b) being residual plot of the predicted value. Table 5presents the statistics summary
of the model in comparison to MRA.
Sustainability 2019, 11, x FOR PEER REVIEW 10 of 17
d. Economic factors e. Environmental factors
F04 F05 F10 Weights
F05 F09 F11 F10 Weights
F04 1 3 2 0.539 F05 1 1 1/2 1 0.204
F05 1/3 1 1/2 0.164 F09 1 1 1/2 1 0.204
F10 1/2 2 1 0.297 F11 2 2 1 1 0.346
CR = 0.01 F10 1 1 1 1 0.246
CR = 0.02
4.2. The Developed iLVM and Its Perfomance
With the computed weights, the LVM’ general Equation (1) was derived to produce a single
layer necessary to produce a land value map. Since the numerical values computed from Equation
(2) have no physical unit except that they only show the relative land values in the study area, these
values were further converted into market values for proper validation and comparison. There were
118 items of market data (min = 1.50; max = 35,000) used to evaluate the developed model. Market
data that represent land value (in the current study) were transformed into logarithmic form (base
10) to address skewed data [33]. Then, a linear relationship between AHP and market values was
assumed [34], initially with an RMSE of 0.547. It was found out that errors occurred at extreme AHP
values (i.e., upper ends); then the model was further improved into a more complex expression
presented in Equation (2) with an improved RMSE of 0.526.
𝐿𝑉𝑀
=
𝑊
𝑊𝑖
𝐹𝑖
+
𝑊
𝑊𝑖
𝐹𝑖
+
𝑊
𝑊𝑖
𝐹𝑖
+
𝑊
𝑊𝑖
𝐹𝑖
+
𝑊
𝑊𝑖
𝐹𝑖
(1)
where WP, WS, WE, WR, and WL are the respective main weights of the physical, social, economic,
environmental, and legal factors; Wi represents the weights of ith sub-factors Fi.
𝐿𝑉𝑀
=
10

.

.

;
𝐿𝑉𝑀
2
.
0
10

.

.

(

)
;
𝐿𝑉𝑀
>
2
.
0
(2)
where LVM is the land value per square meter in the Philippine Peso (PhP). LVM’ is computed using
Equation (1).
The model is evaluated with root-mean-square error (RMSE) and compared with MRA. Figure
5 shows the visual representation of the model performance, with (a) being the fitted line plot
between the actual and predicted value, and (b) being residual plot of the predicted value. Table 5
presents the statistics summary of the model in comparison to MRA.
(a) (b)
Figure 5. (a) The fitted line plot of log land value, and (b)the residual plot of log value
(Philippine Peso, PhP).
0.00
1.00
2.00
3.00
4.00
5.00
0.00 1.00 2.00 3.00 4.00 5.00
Fitted Line Plot (Log Value, PhP)
Actual Predicted
-1.50
-1.00
-0.50
0.00
0.50
1.00
1.50
0.00 1.00 2.00 3.00 4.00 5.00
Residual
Log (Land Value, PhP)
Residual Plot
Figure 5.
(
a
) The fitted line plot of log land value, and (
b
) the residual plot of log value (Philippine
Peso, PhP).
Table 5.
Model statistics summary and significant valuation factors of iLVM and multiple regression
analysis (MRA). RMSE: root mean square error.
Model RMSE Significant factors
iLVM 0.526 All considered
MRA 1.953 F11, F13, F14, F12 1
1Significant at p<0.05 level of significance.
4.3. LV Map of Baybay City, Leyte
Raster layers (2-m resolution) of the 15 factors (Figure 6) were generated using Euclidean distance
tool. The LV map was generated through arithmetic operations in accordance with Equation (1) and
Equation (2) to merge 15 layers into a single layer. In order to derive parcel-level value, the shapefile of
9072 cadastral lots was superimposed over the generated pixel-level map, from which zonal statistics
by mean were then determined. The final LV map, shown in Figure 7(a), shows the seven classes of land
values for straightforward analysis, and was compared with the barangay category map (Figure 7(b)).
Figure 6(a) indicates the 9072 parcels, 984 of which are inside urban barangays, and 2209 of which
have rural high population density. The rest have a rural low population density.
5. Discussions
The relative importance or weights of the five main factors is indicated by the real estate experts
in the Philippines. It is apparent (Table 3) that the social aspect (weight 0.432) is the most important
factor that influences land value, while economic and physical factors are moderately important
(Weight ~ 0.2) in valuing land. This indicates that accessibility nevertheless matters above all in valuing
land, as also reported by previous studies (e.g., [
2
] and [
35
]). It is important to note here that the
social factor comprises other factors that give benefits to society, such as accessibility to road and
basic amenities. The economic factor represents benefits brought by factors due to the proximity
to economic development like business centers. The physical factor refers to physical attributes
Sustainability 2019,11, 3731 12 of 17
(Section 2.2). In other words, these top three factors are regarded as positive influence. On the other
hand, the environment (weight ~ 0.1) is a comparatively less important factor in valuing land. This is
quite realistic because in general, people in the economic industry do not focus much on negative
externalities but rather on accessibility and economic benefits. One study [
36
], for example, reported
that closeness to a fault line is not considered in the valuing of land near the West Valley Fault System
in Philippines. Also, the legal aspect is regarded as the least important factor aecting land value,
perhaps due to lack of law enforcement.
The most influential sub-factors (Table 4) for the physical, legal, social, economic, and environmental
factors are F12 (land use, weight 0.532), F10 (coastline, weight 0.332), F01 (national road, weight 0.223), F04
(CBD, weight 0.539), and F11 (fault zone, weight 0.346), respectively (Table 4). As shown, there is huge
disparity of weights among sub-factors under the physical and economic factors, while the weights are
nearly evenly distributed for legal, environmental, and social sub-factors.
In terms of final model performance, the RMSE of the developed model when compared to actual
data was found to be 0.526, which outperformed the MRA (RMSE =1.953). The significant factors as
identified by MRA are active faultlines (F11), slope (F13), elevation (F14), and land use (F12). The MRA
result, in the current study, contradicts the generally accepted principle that accessibility aects land
value [
2
,
37
] among all factors. On the other hand, the statistics summary of the iLVM, that is, low
RMSE, Adj.R
2
=0.673 and zero average residuals, indicates good fits without bias, and that around
67% of the variability is explained in the model (Figure 5(a)). However, large residuals (e >|1.0|) are
noticeable, causing the predicted value to be under or over predicted (Figure 5(b)). When examined
further, some commercial areas are overvalued because a linear relationship between AHP points and
market value was assumed. In any case, the developed LVM is acceptable.
Visual inspection was also performed to check distribution of high and low values by using
the generated land value (LV) map from the iLVM. After analysis, it was found out that there were
928 parcels with an estimated value of
PhP 10,000 per square meters, around 45% of which fell inside
the urban barangay, while there was a large number (~43%) of parcels coinciding under rural high
population density (i.e., ~300 persons/ha) areas. According to the Comprehensive Land Use Plan of
Baybay City, such a high population density barangay must be classified as an urban area. In general,
the result shows that about 85% of high-valued parcels are commercial establishments and service
industries, while low values are found for agricultural areas.
The result implies that the developed iLVM is acceptable. It is objective, i.e., transparent, consistent,
and flexible to update with respect to time and locality conditions. However, caution must be also
exercised when employing the AHP since the model accuracy is highly dependent on the secondary
data (i.e., geospatial factors). In any case, with the advent of free publicly spatial data such as Open
Street Map, Google Map, and Google Satellite images, updating these data is no longer an issue. It is
therefore safe to say at this point that multi-decision criteria analysis with the AHP can be a perfect
tool for improved land valuation processes.
Sustainability 2019,11, 3731 13 of 17
Sustainability 2019, 11, x FOR PEER REVIEW 1 of 17
Figure 6. Raster layers of 15 geospatial factors considered in the iLVM development.
Figure 6. Raster layers of 15 geospatial factors considered in the iLVM development.
Sustainability 2019,11, 3731 14 of 17
Sustainability 2019, 11, x FOR PEER REVIEW 2 of 17
(a) (b)
Figure 7. (a) Land value map of Baybay City at parcel-level. (b) Barangay map categorized as rural, rural (high population density), and urban per
Baybay City Plan.
Figure 7.
(
a
) Land value map of Baybay City at parcel-level. (
b
) Barangay map categorized as rural, rural (high population density), and urban per Baybay City Plan.
Sustainability 2019,11, 3731 15 of 17
6. Summary and Conclusions
The need for consistent, transparent, realistic, and updated land valuation is essential in all aspects
of land administration, especially when it involves numerous land parcels. The goal of the current
study is to involve experts in various development phases of the innovative land valuation model
(iLVM). The initial stage of the study involved interview with dierent land-related agencies to identify
valuation factors and to assist in drafting of the survey questionnaire. Next, in-person and online
discussions with land and environment experts were conducted to seek advice on both classification
and scoring of 15 factors. Then, another survey questionnaire was prepared for residents that aimed to
supplement sales data and determine land market value in their locality. Lastly, existing laws and
standards, newspapers, and the Internet were reviewed to acquire relevant information related to
environment and legal aspects.
The characteristics of land in terms of its proximity to national road, municipal road, barangay
road, hospital, university, secondary school, CBD, industry/factory, freshwater, coastline, land use,
and an active fault zone as well as its internal features such as land use, elevation, slope, and aspect
were extracted through Python scripting and passed to spreadsheets for analysis. In addition, further
spatial statistical analysis and preparation of final value map were performed in ArcGIS. These factors
are gathered in groups as economic, physical, social, environmental, and legal factors, wherein the
former three are considered as positive while the remaining two as negative with respect to their
influence on land value. The analytic hierarchy process was employed to weigh the 15 factors in terms
of their influence on land value. Actual market values were used to validate the model through a case
study in Baybay City, Leyte. The root mean square error was used to compare model performance
with MRA (RMSE =1.953).
The result shows that iLVM outperformed (RMSE =0.526, Adj. R
2
=0.673) the MRA. The RMSE
was used to evaluate because it reports the absolute fit of the model to the data or the closeness of the
actual value with the predicted values. In other words, the RMSE is absolute measure of fit, while the
R-squared is relative measure. As shown, the iLVM performance is comparable to other methods such
as the MRA [
2
] (Adj. R
2
0.80), GWR [
15
] (Adj. R
2
0.541), and the spatial Bayesian [
14
] (Adj. R
2
0.652),
among others. It is also evident in the final LV map of Baybay City that parcels with high values are
distributed in urban areas where commercial establishment are present, while low-valued parcels are
in agricultural/or forested areas. This implies that iLVM is acceptable. Apart from better accuracy,
other strengths of this approach over existing methods (e.g., [
2
]) are: (1) the involvement of several
land experts in various phases of development (it is logical, transparent, and realistic); (2) factors are
assigned weights in objective way and are hence consistent; and (3) the technique needs little market
data, which is the usual valuation problem [13] and is hence practical.
The drawback of the current approach is that it is less objective than the regression-based
technique (e.g., [
38
,
39
], etc.) but this could be a good alternative of asset valuation [
20
] especially when
there is necessity for a high level of transparency and consistency such as land-related government
transactions [
2
], and when it involved numerous parcels (i.e., mass appraisal). Therefore, the study
concluded that AHP could be a perfect tool for property valuation, and that the performance of
AHP-based valuation can be well-supplemented with existing free publicly data (valuation factors) to
achieve desirable results.
The developed model can be applied in other sub-urban areas of the Philippines with few
adjustments on transforming AHP points to market value. Since the legal factors or conditions set
in this study were based on the laws of Philippines, the iLVM may be modified slightly for application
in other sub-urban areas.
Author Contributions:
For the research conceptualization and design, J.C.B., N.K.T., and H.M.; research
methodology, implementation, validation, formal analysis, writing—original draft preparation J.C.B.; review,
and consultation S.N. and S.M.K.; editing and article review, N.K.T. and H.M.
Funding: This research received no external funding.
Sustainability 2019,11, 3731 16 of 17
Acknowledgments:
The authors wish to express gratitude to the Philippine government oces and agencies
(i.e., Baybay LGU, DENR-LMB, DOST, DPWH, COA, LBP, DBP) for providing the data and relevant technical
information; and to dierent land experts for technical assistance and advice.
Conflicts of Interest: The authors declare no conflict of interest.
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... However, when valuing several properties, commonly known as mass valuation, such conventional methods are mostly insufficient. Advanced methods, such as modern methods (fuzzy logic, artificial intelligence, specialized systems, evolutionary algorithms, and artificial neural networks), and statistical methods (multiple regression, hedonic and nominal) have been applied to mass value applications such as real estate taxation [3,7,[12][13][14][15]. The nominal valuation method remains one of the most used methods for land evaluation, particularly on a large scale. ...
... The factors that influence real estate value must be understood to create a mathematical model. However, the criteria are numerous and vary according to location, even the place where people live and their lifestyles can impact the model developed for mass valuation [2,13,15]. ...
... Nominal valuation is a method that generates calculated parametric scores of weighted parameters that influence real estate values. This method uses scientific approaches to generate a distribution of land values as parametric quantities without requiring the market value [5,6,[11][12][13][16][17][18]. Nominal land value is determined from a large amount of spatial data that may be handled and integrated using Geographical Information Systems (GIS). ...
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Land valuation can be used in various areas, such as taxation, property acquisition, rental, expropriation, urban regeneration, and land readjustment. As a result, determining the value of land accurately by considering multiple criteria to eliminate the impact of subjectivity on the value of land is important. Nominal valuation is a statistical method for determining land values based on multiple criteria. In nominal valuation, criteria can be managed, analyzed, and integrated using Geographic Information Systems (GIS). This study produced a land value map using a nominal valuation method based on multiple criteria. The main criteria and sub-criteria have been determined according to the previous studies. Criteria weights were determined using the Best-Worst Method (BWM), which is one of the Multi-Criteria Decision-Making methods. A nominal land value map of the Atakum district of Samsun was produced by integrating criteria maps using GIS and compared with a value map based on current land sales.
... Therefore, land unit values (LUVs) can be predicted from an ancillary set of environmental or explanatory variables. The 2030 agenda for sustainable development, known as Global Goals, linked the importance of improved land use planning, administration, and management (Bencure et al., 2019); however, defining a precise and perfect valuation model remains a difficult task due to variations in the related factors (Sesli, 2015). Particularly, there is a great interest in assigning values to rural land because rural development is based on the strengthening of land markets. ...
... The need for transparent, realistic, and updated land valuation is essential in all aspects of land administration, especially when numerous land parcels are involved (Bencure et al., 2019). Mass appraisal for the determination of land value has multiple applications, including territorial taxation. ...
... The advantages of having an updated valuation of these lands include: i) greater transparency and legal certainty for owners; ii) fair taxation; iii) more information to design public policies and decide on production investments; iv) better tools to implement tax relief for those affected by natural disasters, and v) the possibility of measuring -social and economic-impacts of climate change. Land is the most precious and limited non-renewable resource; yet, it is one of the most exploited and undervalued natural resources (Bencure et al., 2019). Analysis of the land market can be extremely useful for decisionmakers, since it can be used for designing measures aimed at preserving several types of resources spread across the rural territory (Sardaro et al., 2020). ...
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Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting economic values. New machine learning methods, like tree-based regression models, have been proposed as an alternative to linear regression to predict economic values from ancillary variables, since these algorithms are able to handle non-normality and non-linearity in the data. However, regression trees are commonly estimated assuming independent rather than spatially correlated data. This study aims to build a tree-based regression model that will help to tackle methodological problems related to the determination of prices of rural lands. The Quantile Regression Forest (QRF) algorithm was used to provide a regression model to predict and assess the uncertainty associated with model-derived predictions. However, the classical QRF ignores the autocorrelation underlying spatialized land values. The objective of this work was to develop, implement, and evaluate a spatial version of QRF, named sQRF, for computer-assisted mass appraisal of rural land values accounting for information from neighboring sites. We compared predictions of land values from sQRF with those obtained from spatial random forest, kriging regression, and linear regression models. sQRF performed well in predicting rural land values; indeed, it performed better than multiple linear regression. An important feature of sQRF is its ability to produce a direct uncertainty measure to assess the goodness of the predictions. Land values reflect a complex mix of agricultural returns, localization, and access to markets, which can be predicted from ancillary environmental variables. Good predictive models are essential to determine land values for multiple purposes including territorial taxation.
... This topic-because of its importance to the LC implementation process-has been the subject of many previous studies [13,16]. However, due to the lack of fully satisfactory results, new approaches are still being proposed in this area, including the use of machine learning methods [22,23], analytic hierarchy process (AHP) methods [24], artificial neural networks [25], hedonic pricing models based on linear and nonlinear functions [11], fuzzy logic [15], multicriteria models [12,14,26] and cluster-based methods [27]. Capturing data on agricultural land values also has broader applications than just implementing land consolidation projects. ...
... Property value data and the development of methods to determine them are an important part of modern land management systems [28]. The ever-increasing availability of data, their thematic scope and the development of software are creating indications for building complex expert systems related to mass valuation based on big data processing and geographic information systems [15,25,29], which provides an opportunity to better weigh diverse geospatial factors [24], also using geographically weighted regression [19]. ...
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The article presents the results of the analysis of the legal and practical aspects of the implementation of land value maps for land consolidation in four countries: Slovakia, Croatia, Poland and Turkey. The discussion indicated that it is not possible at present to construct fully universal methods of automatic earth valuation for LC. The reason is that there are too many different approaches to land value mapping. Identification of areas with similar characteristics (valuation factors) needs to be conducted prior to valuation of individual parcels. In both cases, the agronomic value from the farmer’s point of view is the key valuation criterion. It was pointed out that achieving versatility of algorithms can occur only as a result of extensive parameterisation of the developed models, both in terms of the number of factors considered, as well as the manner and strength of their interaction. The development directions of land valuation mass methods should proceed with the widest possible scope of public participation determining the principles of this valuation, which increases the level of acceptance of both the result of the land valuation itself and the subsequent effects of the land consolidation project.
... Studies reviewed focused on the role of real estate practitioners, such as those of Azmi et al. (2014) and Kokot and Gnat (2019) and the valuation of the specific classification of properties by Hicks and Queen (2016) and Crosby and Wyatt (2019). In the Philippines, studies concentrated on models of valuation and problems on taxation by Bencure et al. (2019) and Villaroman (2017), respectively, collections of real property taxes by Zaragoza and Caelian (2020), and implementation of valuation principles by Tumbagahan et al. (2021). There is a dearth of studies focused on the level of knowledge and extent of practice of valuation methods by real estate practitioners; hence, there is a gap in the literature. ...
... The major challenge encountered by real estate practitioners is the influence of property owners in fixing prices in private valuation transactions while in the schedule of market values in the case of government practitioners expressed by all respondents (Tumbagahan et al., 2021). The lack of adequate information and data resulting from withholding market information due to the Data Privacy Act and avoidance of payment of obligations to government is another challenge acknowledged by Bencure et al. (2019) and Effiong (2015), whom both concurred that there is uncertainty and inaccuracy due to inadequate and unreliable data. ...
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We open this new issue for the pandemic year 2022 with articles now indexed in Index Copernicus International in addition to our being indexed in the Directory of Open Access Journals (DOAJ), Crossref, and Asean Citation Index (ACI). Being indexed in these international databases will guarantee more global visibility and accessibility and enhance the citation generation of all published articles in the Philippine Social Science Journal. With that positive note, I am proud to introduce the first quarter journal issue (Volume 5Number 1, January-March 2022). Employing varied quantitative and qualitative approaches, 11 interesting papers explored various topics on politics, educational tourism, well-being, lived experiences of students at risk of dropping out in printed modular distance learning modality, teaching competence of pre-service teachers, learning science process skills and Mathematics during pandemic, digital transformation of the teaching and learning process, risk factors of pneumonia, valuation methods, and cooperatives’ perceived effectiveness in terms of financial and social performance.
... PropTech and other digitalization methods are widely used in real estate valuation to identify factors that affect the cost (Bencure et al., 2019;Mohammed et al., 2021). The use of digitalization methods increases the reliability of the market valuation since the calculation is based on up-to-date information and pricing factors that affect the market value (Melanda et al., 2016). ...
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Cadastral appraisers often use only those pricing factors that are mentioned in the National Standard of Cadastral Appraisal which leads to overestimation or underestimation of the cadastral value, therefore a shortage of budget funds and dissatisfaction among the citizens over the cadastral system of value for the taxation. This study aims to analyze the territory for the presence of local pricing factors, i.e. those that can be identified directly during zoning. Such factors can be, for example, soil quality, environmental health, proximity to waste storage, cell towers, etc. The work consists of the following steps: substantiating the composition of local cadastral value factors, collecting qualitative and quantitative values of cost factors ranking and normalizing the values of factors, checking market data for compliance with the normal distribution law, determining the type of functional dependence of prices on factors, building a model for calculating the cadastral value, analyzing the quality of a statistical calculation model. Approbation was carried out on the example of garden and garden plots located in the Belgorod region of the Russian Federation. The test results showed that the cadastral value model, which included local factors, is statistically significant and better describes the market.
... Renigier-Biłozor [24] proposed an automated valuation model with decision theory and data mining to assess real-estate values. In response to the multidimensional factors that affect land prices, Bencure et al. [25] adopted AHP to integrate multiple factors into one model, in order to achieve the automated valuation of land. It can be seen that the most critical process in automated valuation or batch valuation models is dealing with large-scale parameters, as well as integrating multiple parameters with different characteristics into one valuation model. ...
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The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS (geographic information system) and neural network technologies. This framework can automatically obtain the values of spatial factors affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities.
... Land valuation has recently become a mandatory component in land transactions [1], such as taxation, sale, withdrawal, consolidation [2], as well as in the field of land management, management of land resources [3] and State Land Cadastre management [4]. The value of land depends on physical, economic, social, environmental and legal factors [5]. ...
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The article identifies the main types of land valuation – expert monetary valuation for individual evaluation of particular plots and normative monetary valuation for systematic valuation of land for tax purposes. To conduct the last one, a mass assessment of land plots considering geoinformation technologies is used. It is established that at the present stage of normative monetary valuation of non-agricultural land plots in Ukraine two methodologies are used: within the settlement and outside it. The project of the unified methodology of normative monetary valuation of land plots by combining the existing methods submitted for discussion by the Ministry of Economy, Trade and Agriculture of Ukraine is considered. The values of land plots according to the existing and unified methodologies are studied on the example of four land plots under gas stations, which are located in the city of Kharkiv and outside it. It is established that the values of land plots, determined by existing and unified methods, differ both in the direction of increase and decrease, the difference varies from 9 % to 97 %. It is necessary to conduct additional analysis of initial data and indicators, which are the basis for calculating and improve the offer unified methodology.
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In this study, a new methodology has been developed for a sustainable mass appraisal system. A mathematical model was created with the combination of the Cobb-Douglas and the linear regression model. With the Analytic Hierarchy Process (AHP) method, real estate value criteria were grouped and weighted in a hierarchical structure. The weights obtained with AHP were integrated into the coefficients regarding the criteria weights and densities in the Cobb-Douglas hybrid model. The new hybrid model was confirmed with the features and price equivalents of 435 parcels for sale from the market. Besides, the model analysis results were compared with the Multiple Regression Analysis (MRA) modelling using market prices. While creating the methodology, Geographic Information Systems (GIS) was used to organize the geographic and regional data of the region. After developing the new hybrid model, criteria groups that developed the model and relevant sub-criteria were evaluated using Pearson's correlation analysis.
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Coconut Creek (Florida), ISSN 0749-0208. This study aims to infer the possibility of inundation of coastal areas due to sea level rise caused by climate change. In addition, it seeks to present a necessary survey method and value calculation method to be reflected on the land value. To this end, GIS spatial analysis was performed using tidal observation data and flooding maps. The analysis of IPCC sea level rise assuming the worst-case scenario (Scenario 1) and the gradual-case scenario (Scenario 2) of the actual measurement data of the Busan Tide Observatory showed that 3.8%-5.5% of the total land in Haeundae-gu will be at risk of flooding within the next 100 years. Particularly, lands with a relatively high value at present are more vulnerable. If the method of calculating flood-prone areas presented in this study is introduced in the Government's Land Characteristic Evaluation and the standard land value for areas expected to be flooded is accurately assessed, then the economic loss and social confusion caused by future sea level rises can be prevented. © 2021 Coastal Education Research Foundation Inc.. All rights reserved.
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Construction or purchase of a detached house in the suburb, which is a more comfortable and demanded residential option for families, is really topical nowadays. According to this fact, large and small gated communities appear around large cities. They include modern infrastructure, protected territory and other amenities. The study analyzes 38 economy and business class gated communities around the city of Kazan. This analysis allowed us having the most complete understanding of events in this segment of residential real estate. The article selects pricing factors and reveals a mathematical pattern of changes in the market value of land plots intended for private houses construction in Kazan. As a result, the study reveals that three factors have the greatest impact on the value of land plots: distance from the city, transport accessibility and the current lifespan of the gated community. The materials of the article may be of practical importance for developer companies, as well as for potential buyers of land plots in gated communities in the Republic of Tatarstan.
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Predicting the value of real estate is a complex endeavor due to the abundance of subjective criteria. Objective consideration of the value-affecting criteria in real estate and regulation of decision support systems will enable the acquisition of more accurate results. In this study, analytic hierarchy process (AHP), a type of multi-criteria decision analysis (MCDA), is used to reproduce coefficients that serve as the basis for real estate valuation. A region in the Selcuklu district of Konya, Turkey was used to test the model created by AHP. Weighted criteria describing areas subjected to purchase/sale were generated by the AHP method and then validated. Additionally, a valuation model was created by the multiple regression analysis (MRA) method for comparison and performance analyses. Weighted values were transformed from AHP points and acquired from the MRA method and then joined with geographic information systems (GIS). Value maps of the study area and purchase/sale values were generated according to these newly created models. The performance comparison and value maps revealed that the AHP method is more successful than the MRA method. This study addressed the complexity of criteria issue by using the original hierarchical structure of AHP and thus contributes to the world economy by enabling the generation of more accurate estimations.
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School buildings must be resilient during hazardous events like earthquakes so that the important functions of the school will not be affected. During disasters, schools have an added value and important function in post-disaster activities. Schools are often used as evacuation centers. When schools are damaged, the school’s mission of continuous delivery of education will be disrupted. Due to the inadequate number of structures in our country which are intended for evacuation during disasters, schools have been used in some cases as evacuation centers, again disrupting the school’s operations. To assure that school buildings will be operational in times of disaster, structural vulnerability assessment and appropriate retrofitting must be carried out. Due to budgetary constraints in most schools, a prioritization scheme must be devised to identify the buildings that must be given immediate attention. A rapid visual screening on the structural vulnerability due to earthquake hazards can be done and then rank the buildings for more detailed inspection and retrofitting. To refine the screening and ranking, the functional asset value of the buildings can be used as a second criterion. In a post-disaster scenario, school buildings have two important functional asset values: (a) Educational Functional Value and (b) Emergency Functional Value. The educational function focuses on “continuous learning” and consists of continuous conduct of classes, preservation of school records and documents for future use, and availability of basic resources and access to basic facilities. Emergency function focuses on “protecting lives” and consists of post-disaster uses of the school such as an evacuation center, storage of relief goods and an operation center. This study aims to develop a method of assigning an index corresponding to a school building’s post-disaster functional asset value using the Analytical Hierarchy Process (AHP) and an expert’s survey. Moreover, using the two-level vulnerability screening as a prioritization scheme, decision makers can prioritize the buildings that have high seismic risk and high functional asset value. This methodology was applied on a school campus as a case study.
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Land valuation is a highly important concept for societies and governments have always emphasis on the process especially for taxation, expropriation, market capitalization and economic activity purposes. To success an interoperable and standardised land valuation, INSPIRE data models can be very practical and effective. If data used in land valuation process produced in compliance with INSPIRE specifications, a reliable and effective land valuation process can be performed. In this study, possibility of the performing land valuation process with using the INSPIRE data models was analysed and with the help of Geographic Information Systems (GIS) a case study in Pendik was implemented. For this purpose, firstly data analysis and gathering was performed. After, different data structures were transformed according to the INSPIRE data model requirements. For each data set necessary ETL (Extract-Transform-Load) tools were produced and all data transformed according to the target data requirements. With the availability and practicability of spatial analysis tools of GIS software, land valuation calculations were performed for study area.
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This study targets a research on the application of artificial neural network (ANN) and multiple regression analysis (MRA) approaches in Geomatics Engineering science to land valuation process. The prediction capability was investigated and evaluated using three ANN models constructed with different activation functions (sigmoid, tangent hyperbolic and adaptive activation function) and MRA was used as a reference approach. These four methodologies were applied to land valuation in order to model the unit market value with various inputs based on essential criteria. All approaches were investigated with their estimation level in training and testing data. It was observed that adaptive ANN performed noticeably higher predicting the values with the highest accuracy and giving the smallest RMSE value in validation process, although other methodologies approximated to the raw data at a promising level for further valuation-based applications.
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There are principally two ways for quantifying the land value of parcels in land consolidation schemes. The first approach involves assigning an agronomic value based on soil quality and land productivity represented by a score while the second method determines the market value signified in monetary terms. In Cyprus, the market value is employed, which is defined through an empirical process based on visual inspection of all parcels and hence it constitutes a type of mass land appraisal. This process presents weaknesses regarding time, costs, transparency, accuracy, reliability, consistency and fairness. In addition, the lack of adequate sales transactions in rural areas further complicates the whole process. Consequently, these deficiencies have adverse effects in the preparation of land consolidation plans and cause arguments between landowners and the authorities carrying out each scheme. Although experts are aware of this issue, there is a lack of research investigating land valuation factors and the quality of this traditional process. Therefore, this paper discusses, explores and assesses the land valuation undertaken by the Land Valuation Committee (LVC) in a case study area in Cyprus and proposes a new framework for carrying out this process. The assessment of the current process is undertaken by employing advanced spatial analysis techniques, including multiple regression analysis (MRA) and geographically weighted regression (GWR) within a GIS. Results show that eight out of fourteen land valuation factors related to parcel location characteristics, legal factors, physical attributes and economic conditions are the most significant. In addition, although the basic regression fits are quite good, some of the assumptions required for testing the hypothesis are not met, indicating unreliability and inconsistency in the relationships modelled. Furthermore, the presence of spatial autocorrelation reveals important regional variation in these factors suggesting significant inconsistencies in the valuation policy applied by the LVC. The latter two findings confirm experts’ concerns and suggest the need for a new land valuation framework that is designed to overcome the problems of the current process. The application of this framework and the investigation of various critical relevant issues is the core of ongoing further research.
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Determining the real estate evaluations and reflecting them on taxations are among the most important economic resources of the developed countries. In Turkey, the system is not able to ground the real estate evaluations on scientific criteria yet, which causes various problems in applications regarding real estate evaluations (such as estate tax, expropriation, court surveillances) and an important economic loss.Thus, it is required to generate tax-base real estate evaluation maps within the scope of the legal legislation in Turkey. This study aimed to generate a fast, up-to-date and dynamic evaluation map that would form a base for the real estate taxation. The closeness of real estates to the technical infrastructure and social equipment areas and their variety affect the real estate evaluations either positively or negatively and form the local benefit for real estates. This study determined the areas (such as main roads, green spaces, trading areas and urban attraction centers) affecting the evaluations of real estates depending on their positions. In order to make position-based decisions about the data being stored in the Geographical Information System, the geographical data were questioned and monitored with analyses. The acquired data were exposed to necessary analyses in the relevant modules of the GIS programs, which enabled us to grade the factors affecting the evaluation for each parcel and try to generate real estate evaluation maps depending on the evaluation-effect factors to be selected as dynamics. Parcel-based real estate evaluations were determined by imposing vector-based cadastral maps on these maps being generated. In this study, a raster real estate evaluation map was generated in unstructured parcels of a sample neighborhood via the scoring method and with the help of the Multi-Criteria Decision-Making Analysis and both environmental and social factors. This system could enable us to question and analyze the features of parcels and thus, rapidly change them according to the system variables.
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Bangkok Metropolitan Area, the capital of Thailand, is known as one of the world's most traffic-congested cities. Lots of transport-related projects to alleviate traffic congestion especially rapid transit system always require an amount of land. Therefore, several privately owned lands have to be acquired by the government agencies due to the lack of available spaces in an urban area. However, the assessment of compensation for a compulsory land acquisition is determined on the basis of the assessor's database that often values each of properties lower than expected. In this paper, the assessed prices data and the offering prices for sale data of Bangkok are analyzed with the use of regression framework through the hedonic pricing model. The spatial non-stationarity to examine the variations of the implicit effects to property value is also included bases on the geographically weighted regression (GWR) technique. The results show that the determinants of property value are myriad and varied over space, i.e. spatial non-stationarity exists in the study area. The obtained results indicate that the difference between two data is extremely large. These results provide the basic information to compute the fair compensation for the landowners and allow the government agencies to tax the direct beneficiaries of their investments in advance so as to finance infrastructure projects.
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The opacity of the farm market means that valuations are based primarily on expert estimates rather than on actual transaction prices. The valuation method based on the two cumulative distribution functions (VMTCDF), created by Ballestero (1971), improves the synthetic method based on estimating the market value of an asset by establishing a proportional relation between the asset and one external variable. However, in most cases the expert must consider multiple external variables. This paper proposes a definitive extension to k indexes with a methodology particularly applicable to the field of valuation of non-market goods or markets where little information is available as may be the case with the valuation of agricultural land. The contribution is illustrated with an empirical example.
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In recent years, the employment of multiple criteria decision analysis (MCDA) techniques in solving complex real-world problems has increased exponentially. The willingness to build advanced decision models, with higher capabilities to support decision making in a wide range of applications, promotes the integration of MCDA techniques with efficient systems such as intelligence and expert systems, geographic information systems, etc. Amongst the most applied MCDA techniques are Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The development of a comprehensive perspective on research activities associated with the applications of these methods provides insights into the contributions of countries, institutes, authors and journals towards the advancements of these methods. Furthermore, it helps in identifying the status and trends of research. This in turn will help researchers in shaping up and improving future research activities and investments. To meet these aims, a bibliometric analysis based on data harvested from Scopus database was carried out to identify a set of bibliometric performance indicators (i.e. quantitative indicators such as productivity, and qualitative indicators such as citations and Hirsch index (h-index)). Additionally, bibliometric visualization maps were employed to identify the hot spots of research. The total research output was 10,188 documents for AHP and 2412 documents for TOPSIS. China took a leading position in AHP research (3513 documents; 34.5%). It was also the leading country in TOPSIS research (846 documents; 35.1%). The most collaborated country in AHP research was the United States, while in case of TOPSIS it was China. The United States had gained the highest h-index (78) in AHP research, while in TOPSIS it was Taiwan with h-index of 46. Expert Systems with Applications journal was the most productive journal in AHP (204; 2.0%) and TOPSIS research (125; 5.2%), simultaneously. University of Tehran, Iran and Islamic Azad University, Iran were the most productive institutions in AHP (173; 1.7%) and TOPSIS (115; 4.8%) research, simultaneously. The major hot topics that utilized AHP and will continue to be active include different applications of geographic information systems, risk modeling and supply chain management. While for TOPSIS, they are supply chain management and sustainability research. Overall, this analysis has shown increasing recognition of powerful of MCDA techniques to support strategic decisions. The efficacy of these methods in the previous context promotes their progress and advancements.