Access to this full-text is provided by MDPI.
Content available from Sustainability
This content is subject to copyright.
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 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).
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 effects 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 difficult 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 difficult 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 officials concerned with land resources in the
different 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 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].
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 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).
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 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.
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 Difference 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 =(10−0.5335 +1.2023LVM0;LVM0≤2.0
10−0.1858 +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 affecting 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 affects 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 different 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 offices and agencies
(i.e., Baybay LGU, DENR-LMB, DOST, DPWH, COA, LBP, DBP) for providing the data and relevant technical
information; and to different land experts for technical assistance and advice.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
ELD Initiative. The Value of Land: Prosperous Lands and Positive Rewards through Sustainable Land
Management. 2015. Available online: www.eld-initiative.org (accessed on 4 March 2018).
2.
Demetriou, D. The assessment of land valuation in land consolidation schemes: The need for a new land
valuation framework. Land Use Policy 2016,54, 487–498. [CrossRef]
3.
Omari, M.A. The Role of Reliable Land Valuation Systems in Land Management and Land Administration
Systems efficiency. In FIG Working Week 14–19 June 2008: Integreting Generations; FIG (International Federation
of Surveyors): Stockholm, Sweden, 2008.
4.
Tsoodle, L.J.; Featherstone, A.M.; Golden, B.B. Combining Hedonic and Negative Exponential Techniques to
Estimate the Market Value of Land. Agric. Financ. Rev. 2007,67, 225–239. [CrossRef]
5. Dale, P.; McLaughlin, J. Land Administration; Oxford University Press, Inc.: New York, NY, USA, 2003.
6.
Lavee, D.; Baniad, G. Assessing the value of non-marketable land: The case of Israel. Land Use Policy
2013
,
34, 276–281. [CrossRef]
7.
Sesli, F.A. Creating real estate maps by using GIS: A case study of Atakum-Samsun/Turkey. Acta Montan. Slovaca
2015,20, 260–270.
8.
Bogataj, M.; Suban, D.T.; Drobne, S. Regression-fuzzy approach to land valuation. Cent. Eur. J. Oper. Res.
2011,19, 253–265. [CrossRef]
9.
Herrerias, J.M.; Herrerias, R. Valuation method for land pricing based on two cumulative distribution
functions. Span. J. Agric. Res. 2010,8, 538–546. [CrossRef]
10.
Schulz, R.; Wersing, M.; Werwatz, A. Automated valuation modelling: A specification exercise. J. Prop. Res.
2014,31, 131–153. [CrossRef]
11.
Yalpir, S. Enhancement of parcel valuation with adaptive artificial neural network modeling. J. Artif. Intell. Rev.
2016, 1–13. [CrossRef]
12.
Naud
é
, S.D.; Kleynhans, T.E.; van Niekerk, A.; Ellis, F.; Lambrechts, J.J.N. Application of spatial resource
data to assist in farmland valuation. Land Use Policy 2012,29, 614–628. [CrossRef]
13.
Garcia, C.; Garcia, J.; Lopez, M.M.; Salmeron, R. A generalized method for valuing agricultural farms under
uncertainty. Land Use Policy 2017,65, 121–127. [CrossRef]
14.
Cotteleer, G.; Stobbe, T.; van Kooten, C.G. A Spatial Bayesian Hedonic Pricing Model of Farmland Values.
J. Agric. Econ. 2008, 1–8. [CrossRef]
15.
Manganelli, B.; Pontrandolfi, P.; Azzato, A.; Murgante, B. Using geographically weighted regression for
housing market segmentation. Int. J. Bus. Intell. Data Min. 2014,9, 161–177. [CrossRef]
16.
Hu, S.; Yang, S.; Li, W.; Zhang, C.; Xu, F. Spatially non-stationary relationships between urban residential
land price and impact factors in Wuhan city, China. J. Appl. Geogr. 2016,68, 48–56. [CrossRef]
17.
Ahn, J.J.; Byun, H.W.; Oh, K.J.; Kim, T.Y. Using ridge regression with genetic algorithm to enhance real estate
appraisal forecasting. Expert Syst. Appl. 2012,39, 8369–8379. [CrossRef]
18.
Kuburi´c, M.; Tomi´c, H.; Ivi´c, S.M. Use of Multicriteria Valuation of Spatial Units in a System of Mass Real
Estate Valuation. Kartogr. Geoinf. 2012,11, 58–74.
19.
Antipov, E.A.; Pokryshevskaya, E.B. Mass appraisal of residential apartments: An application of Random
forest for valuation and a CART-based approach for model diagnostics. Expert Syst. Appl.
2012
,39, 1772–1778.
[CrossRef]
20.
Aragon
é
s-Beltr
á
n, P.; Aznar, J.; Ferr
í
s-Oñate, J.; Garc
í
a-Mel
ó
n, M. Valuation of urban industrial land:
An analytic network process approach. Eur. J. Oper. Res. 2008,185, 322–339. [CrossRef]
21.
Aydinoglu, A.C.; Bovkir, R. An approach for calculating land valuation by using INSPIRE data models.
Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017,42, 19–21. [CrossRef]
22.
O’geen, A.T.; Southard, S.B.; Southard, R.J. Revised Storie Index for Use with Digital Soils Information.
Agric. Nat. Resour. Publ. 2008. [CrossRef]
Sustainability 2019,11, 3731 17 of 17
23.
Verheye, W.H. Use of land evaluation techniques to assess the market value of agricultural land. Agropedology
2000,10, 88–100.
24.
Hu, S.; Cheng, Q.; Wang, L.; Xu, D. Modeling land price distribution using multifractal IDW interpolation
and fractal filtering method. J. Landsc. Urban Plan. 2013,110, 25–35. [CrossRef]
25.
Jahanshiri, E.; Buyong, T.; Shariff, A.R.M. A review of property mass valuation models. Pertanika J. Sci. Technol.
2011,19, 23–30.
26.
Vaidya, O.S.; Kumar, S. Analytic hierarchy process: An overview of applications. Eur. J. Oper. Res.
2006
,169,
1–29. [CrossRef]
27.
Philippine Statistics Authority. Road Classification. 2017. Available online: https://psa.gov.ph/content/road-
classification (accessed on 2 February 2019).
28.
Zyoud, S.; Fuchs-Hanusch, D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst. Appl.
2017,78, 158–181. [CrossRef]
29.
Kocharyan, G.G.; Kishkina, S.B.; Ostapchuk, A.A. Seismogenic width of a fault zone. Dokl. Earth Sci.
2011
,
437, 412–415. [CrossRef]
30.
Ilumin, R.C.; Oreta, A.W.C. A Post-Disaster Functional Asset Value Index for School Buildings. Procedia Eng.
2018,212, 230–237. [CrossRef]
31.
Baybay City Local Goverment Unit. Comprehensive Landuse Plan 2009—2020 of Baybay City, Philippines;
Baybay City Local Government: Leyte, Philippines, 2010.
32.
Bureau of Local Government Finance—Department of Finance Philippines. LGU Taxation and Revenue
Practices; Bureau of Local Government Finance: Manila, Philippines, 2015.
33.
Ma, S.; Swinton, S.M. Valuation of ecosystem services from rural landscapes using agricultural land prices.
Ecol. Econ. 2011,70, 1649–1659. [CrossRef]
34.
Bunyan Unel, F.; Yalpir, S. Valuations of Building Plots Using the Ahp Method. Int. J. Strateg. Prop. Manag.
2019,23, 197–212. [CrossRef]
35.
Nilsson, P.; Johansson, S. Location determinants of agricultural land prices. Rev. Reg. Res.
2013
,33, 1–21.
[CrossRef]
36.
Galeon, F.; Albano, M. The Effect of Proximity to Fault Line as a Land Value Determinant in Metro.
In FIG Working Week 2016: Recovery from Disaster; FIG (International Federation of Surveyors):
Christchurch, New Zealand, 2016.
37.
Wyatt, P.J.; Wyatt, P.J. The development of a GIS-based property information system for real estate valuation.
Int. J. Geogr. Inf. Sci. 1997,11, 435–450. [CrossRef]
38.
Malaitham, S.; Fukuda, A.; Vichiensan, V.; Wasuntarasook, V. Hedonic pricing model of assessed and market
land values: A case study in Bangkok metropolitan area, Thailand. Case Stud. Transp. Policy
2018
. [CrossRef]
39. Kilpatrick, J. Expert systems and mass appraisal. J. Prop. Invest. Financ. 2011,29, 529–550. [CrossRef]
©
2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Content uploaded by Jannet Bencure
Author content
All content in this area was uploaded by Jannet Bencure on Jul 08, 2019
Content may be subject to copyright.
Content uploaded by Jannet Bencure
Author content
All content in this area was uploaded by Jannet Bencure on Jul 08, 2019
Content may be subject to copyright.
