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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 countrys 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 experts 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 experts 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
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 citys 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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... Firstly, methods of applying GIS technology include geographically weighted regression (GWR) (Hui, 2010;O'Sullivan, 2003;Yu-hon, 2014;Wang et al., 2019;Demetriou, 2016). Secondly, building a model to determine land prices through the application of GIS technology combined with the Analytic Hierarchy Process (AHP) (Li et al., 2015;Bencure et al., 2019Bencure et al., , 2022, Fuzzy Analytical Hierarchy Process (FAHP) is essential (Renigier-Biłozor et al., 2019;Bovkir & Aydinoglu, 2018). However, the disadvantage of using the above method is that expert opinions greatly influence the model. ...
... The results of research in District 9, Ho Chi Minh city (Thuy, 2017) and Nghi Tan ward, Cua Lo town, Nghe An province have shown that the larger the width of the contiguous road, the higher the land price. With the factor of distance to the hospital (VT6), research results using the Analytic Hierarchy Process method at Baybay City, Philippines (Bencure et al., 2019) and using the Bayesian Model Average in Quoc Oai district, Ha Noi City (Doan, 2023) have calculated that the greater the distance to the hospital, the lower the land price and vice versa. Planning eISSN: 2300-5289 | Received 2023-10-03 | Revised 2024-04-03 | Accepted 2024-04-05 77 information (PL1) is essential for developing areas, such as suburban areas, seeing as how information related to planning always attracts the attention of most real estate investors. ...
... In addition, the developed model can be applied in other suburban areas of the central region of Vietnam because of the same characteristics in terms of socio-economic conditions and speed of development. Using the regression model to determine land prices in suburban areas is appropriate because this is an objective method and more accessible when it comes to application than the Analytic Hierarchy Process method (Bencure et al., 2019). However, to further improve the accuracy of results, there is a need to carry out other research methods in suburban areas to enable comparison with the results of the regression model method so as to determine which is most suitable. ...
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The paper aims to promote a model for determining residential land prices within a suburb of Hue city, Vietnam to improve the efficiency of land price determination and management. The study conducted surveys of 27 variables of 200 residential land plots in order to run the hedonic model using SPSS 26.0 software. The result developed a model that explained 78% of the change in land prices by incorporating five factors: area of land, width of road attached to the land plot, distance to hospital, ability to generate cash flow and planning information. Meanwhile, the writers also produced a map of residential land prices in the 2022 suburban areas of Hue city, Vietnam. This is a helpful tool in land market consulting and land valuation procedures.
... In emerging markets such as China, a pilot property tax experiment is taking place, but the establishment of a property tax system is complicated, involving not only relevant policies and laws, but also a valuation mechanism and methods [2]. A recent study of the property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on mass appraisal methods [3], such as the multilevel model [4], spatial analysis [5][6][7][8][9], heuristic expert systems [10], and the comparative analysis of multiple econometric models [11]. The authors of ref. [12] conducted a study that is highly representative in this field. ...
... The fifth category, moderately sensitive to distance, but more so to quantity, involves quantifying by counting and layer-graded scoring of the number of certain features within a specific range around the sample case, such as banks, shopping malls, supermarkets, convenience stores, restaurants, fast food outlets, beverage shops, cinemas, sports facilities, scenic spots, and parks. Overall, this study analyzes research outcomes directly related to this section by referencing empirical values of feature indicator adjustment coefficients used in the practical operations of individual real estate appraisals [6,8,9,12,15,22,27], and the quantification rules and expected theoretical impact signs for individual, locational, and socio-economic characteristics are shown in Table 4. (3), two year (2), less than two year (1) + Location characteristics -- (14), Changning (13), Yangpu (12), Hongkou (11), Putuo (10), Pudong (9), Minhang (8), Baoshan (7), Qingpu (6), Songjiang (5), Jiaidng (4), Fengxian (3), Chongming (2) The aforementioned quantitative approach quantifies the real estate characteristic indicators from the perspectives of quantity and distance, without taking into account the impact of the quality of these indicators on prices, such as the levels of educational and lifestyle facilities. To achieve more detailed quantification, it would be necessary for evaluators to make appropriate fine-tunings to the quantification process, or to further refine the process with support from more comprehensive data. ...
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Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal.
... Generally, AVMs can involve various types of real estate valuation procedures commonly used in real estate appraisals (e.g., deterministic procedures, linear or nonlinear regression, artificial neural networks (ANNs)) [20][21][22][23][24]; in this research, the chosen valuation method is a deterministic procedure, namely, the mono-parametric appraisal procedure. ...
Chapter
Recently, the use of IT tools in the assessment of real estate properties has become very popular among appraisal specialists, bankers, and researchers in this field. Many automatic valuation models (AVMs) using different statistical and mathematical models, such as regression, neural networks, and fuzzy logic, and based on different technologies have been proposed and developed with the purpose of appraising the value of a property. In this paper, combining GIS tools and the basic principles on which the market-oriented approach is based, we suggest a GIS-based AVM that is useful for assessing land market value, believing that the complex nature of this type of property can be well described by the aforementioned GIS tools. We argue that the development of this model provides useful insights for public administrations to support them in the definition of management actions of the local real estate sector concerning, for example, practical purposes such as tax or expropriative compensation quantification.
... Municipalities can instruct property tax assessors to list improvement value and land value separately, even if the tax structure remains the same. While accurate land value estimation is often noted as a hindrance to the implementation of a LVT, recently the use of mass appraisal techniques has drastically streamlined the process, e.g., [97,98]. Likewise, given the significant impact of speculative investment on sprawl, municipalities might consolidate public information on property purchases and local investment behaviors to assess the ubiquity of speculation-based land purchases. ...
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Sprawling land development patterns have exacerbated ecological degradation, social fragmentation, and public health problems. Perverse incentives arise from the ability to privatize collectively created value in land rents and socialize ecological costs. Land value taxation (LVT) has been shown to encourage urban infill development by reducing or eliminating rent-seeking behavior in land markets. However, despite its purported benefits, this tax reform is value monistic in its definition of optimal land use and, therefore, does little to address the lack of non-market information to inform land use decisions. We propose an ecological-impact-weighted land value taxation policy (ELVT) which incorporates the ecological footprint of land use into one’s land value tax burden. We test both proposed policies (LVT and ELVT) relative to a “status quo” (SQ) property tax scheme, utilizing a conceptual spatially explicit agent-based model of land use behaviors and housing development. Our findings suggest that both tax interventions can increase the capital intensity and decrease the land intensity of housing development. Furthermore, both tax interventions can lead to a net profit loss for speculators and a decrease in the average housing unit price. The ELVT scheme is shown to significantly increase urban nature provisions and dampen the loss of ecological value across a region.
... The literature has attempted to define a sustainable tax and a sustainable tax system, and to propose their classification and evaluation criteria [27,44]. A sustainable tax is a financial burden contributing to sustainable development, i.e., to simultaneously achieving fundamental (economic, socio-cultural, and ecological) and administrative goals [45,46]. ...
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This study presents a novel approach for developing a sustainable property tax system, aimed at enhancing economic stability and promoting sustainable regional development. This research employs a phenomenological methodology, which includes a comprehensive review of the scientific and practical literature, and their critique and synthesis. The authors also draw on their experiences with the tax system transformation within their own country. This study explores the integration of a consensual governance approach and the concept of antifragility into the complex issue of property taxation. The primary objective is to design a property tax management model that not only fulfills its economic functions, but also fosters an antifragile taxpayer society, contributing to the creation of a resilient and socially cohesive community. The findings demonstrate that a consensual and transparent property tax system, actively involving local stakeholders in decision-making processes, not only reduces resistance to tax reforms but also strengthens a community’s ability to adapt to economic fluctuations. By integrating the principles of good governance and sustainable development, the proposed model promotes socio-economic stability and provides a flexible framework that can accommodate diverse stakeholders needs, ultimately benefiting the broader community through enhanced social cohesion and long-term sustainability.
... Mathematical methods combined with spatial analyses based on GIS represent another category of automatic assessment models. Various mathematical methods can be found in the literature, such as regression analysis [1,81], other statistical methods [10,25,82], or interpolation methods [82], but algorithms based on fuzzy logic [6,83] are most commonly used to reduce subjectivity in the evaluation process. ...
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In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development.
... La Regresión Lineal Múltiple (RLM) es una técnica comúnmente utilizada para la VMI que permite determinar las relaciones lineales existentes entre una variable dependiente y variables independientes: la variable dependiente normalmente es el precio, mientras que las variables independientes pueden ser distancias a diferentes puntos de valorización, inclusión en zonas con altos índices de aprovechamiento edilicio o con afectaciones ambientales, entre otras. La RLM ha sido utilizada por varios autores como base para comparar su rendimiento frente a otras técnicas emergentes (Bencure et al., 2019;Cohen et al., 2020;Doumpos et al., 2020;Wang et al., 2020;Yilmazer & Kocaman, 2020), y si bien es de fácil comprensión, su aplicación para generar modelos de VMI para toda una ciudad no siempre se torna apropiada puesto que, eventualmente, no tiene la capacidad de identificar los elementos determinantes para la concretización de un negocio inmobiliario, generando frecuentemente problemas relacionados con autocorrelación espacial de los residuos y heterocedasticidad (Kauko & d'Amato, 2017). ...
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La necesidad de valuar masivamente los inmuebles se ha comprobado en el desarrollo de las políticas públicas en general. Los métodos tradicionalmente aplicados para la valuación masiva de inmuebles (VMI) han involucrado el uso de fórmulas e índices complejos, difíciles de obtener, procesar y mantener, elegidos en parte por la falta de conocimiento en tecnologías de automatización de procesos. Los procedimientos de VMI no se realizan con una frecuencia apropiada para acompañar la dinámica del mercado inmobiliario y por ese motivo nunca se llega a una valuación precisa de los inmuebles. En búsqueda de soluciones a dicha problemática, se desarrolló este trabajo siguiendo tres acciones: 1) generar una base de datos de entrenamiento capturando, sistematizando y procesando datos de oferta de inmuebles en el mercado inmobiliario de tres ciudades intermedias de la provincia de Mendoza; 2) caracterizar las bases de entrenamiento y predicción mediante variables geográficas; 3) asignar valores a todas las parcelas urbanas a través de técnicas de Machine Learning, más concretamente a través del algoritmo XGBoost para el modelado de valores. Aun cuando este recurso ha sido poco explorado en este ámbito de aplicación, los resultados y métricas obtenidas muestran que su utilización deriva en parámetros de calidad aceptables para los objetivos planteados, y que su implementación permite diseñar estrategias eficientes y eficaces para la construcción de VMI, a menor costo en dinero y en tiempo que los métodos tradicionales. La clave del éxito, en parte, tiene fuerte dependencia con la estrategia de recolección de datos.
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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.