17-03-2014, 05:38 PM
Abstract— This paper presents an appraisal model of real
estate in Thailand using fuzzy logic which is a method for a
capable of solving any value in blank land submarkets relative
to clustering methods based on classic (or crisp) set theory. The
valuer shall record the inspection result regarding condition
and location of such land. Systematic analysis shall be applied
and the fact of the land’s condition will also be recorded.
Generally, such valuation data will be taken to compare with
other comparable data and then classified by the valuer
according to its significance, namely, A, B, C and D. This
classification criteria is still not clear, especially weightedfactor
of such property (Effective Factor of Property).
Therefore, this problem has drawn attention from the
researcher to explore the causes of such problem and at the
same time propose models for clustering using fuzzy as a tool
for classifying the components of the property (Effective
Factor of Property) in order to determine upon such vagueness
regarding weighted-factor. Issues of choosing algorithm
parameters are discussed on the basis of applying fuzzy
clustering to 101 metropolitan areas in the Thailand. The
result from the experiment shows that the components of the
property are weighed more appropriately and closely to the
real value which can give the percentage of reliance to be at
approximately 97 %. This enables the valuer to determine and
make a comment on property value that its evaluated value
becomes closer to the real one to the greatest extent.
Keywords-appraisal; fuzzy; real estate; effective factor of
property
I. INTRODUCTION
To perform property valuation, the valuer has always been
troubled with how to value such property. This is mostly
resulted from variation in effective factors of property such
as location, communication & access, public utility, high
test & best use and site of property. These problems have
made the valuer to take into account for analyzing and
comparing with the comparable data and led to be reluctant
in weighing each of those factors which may ultimately give
rise to inaccurately giving weight to such significant factors.
This study attempts to examine the potential of fuzzy
clustering in enriching methods for identifying housing
submarkets. Identifying housing submarkets have many
pragmatic values. It concisely reveals socio-demographic
structure of city, and helps understand geographic process
surrounding neighborhood formation. The most commonly
used method for identifying land. Clustering methods can be
roughly classified into hierarchical method and partitioning
method. Partitioning clustering can be divided into
exclusive and overlapping methods depending on which set
theory the algorithm is built on. Exclusive clustering (e.g.,
k-means) is built on classic set theory where an element is
an exclusive member of a set. Overlapping clustering is
based on fuzzy set theory where an element can be a
member of one or more sets. For instance, some aggregate
unit of housing (mainly delineated by census unit) is
composed of a mix of different housing types and diverse
demographics. In such cases, it is logical to consider that the
housing unit belongs to more than one housing submarket.
This study examines whether fuzzification offers any
advantage to methodology for land market segmentation
over counterpart based on classic set theory.
In the year 1998, models demonstrating decision
making behavior in the real estate market had been
proposed. Such models were clearly the basic model
reflecting data of real estate market which arguably made
the agent to perceive that the fastest respond in the market
would depend on the consumer’s behavior as the most
significant variable for property valuation. This was done
through model for valuation which is the program giving
accuracy and proved to give a successful result representing
the data of Edmonton, Alberta, Canada. [1]
In 2006, forecasting model, FNN, has been developed
from studying the history of relation between the reality of
buying and selling of real estate. Above all, such model
could forecast and give level of value of real estate. This
developed FNN forecasting model, from the experiment,
reflected its capability of valuing new property and its
technique is also increasingly interested by the valuer. [2]
In 2007, the study was done to categorize group of
urban residential property which is the modified procedure
influenced by membership of market of welfare housing and
was the comparison between the result of fuzzy clustering
and crisp set selecting 85 urban areas in the U.S. From such
comparison study, it was found that between fuzzy
clustering and hard clustering, the former had represented its
major characteristics to a more concise extent which was
appropriate for supporting proposed method of classification
of the housing market. [3]
In 2008, it is the year for online business of real estate
development. The market has shown its dynamic character
from 2 reasons: 1. increase of customer 2. investment in real
estate industry sector. The researcher has spent time to
emphasize on Multi-Criteria for real estate website.
estate in Thailand using fuzzy logic which is a method for a
capable of solving any value in blank land submarkets relative
to clustering methods based on classic (or crisp) set theory. The
valuer shall record the inspection result regarding condition
and location of such land. Systematic analysis shall be applied
and the fact of the land’s condition will also be recorded.
Generally, such valuation data will be taken to compare with
other comparable data and then classified by the valuer
according to its significance, namely, A, B, C and D. This
classification criteria is still not clear, especially weightedfactor
of such property (Effective Factor of Property).
Therefore, this problem has drawn attention from the
researcher to explore the causes of such problem and at the
same time propose models for clustering using fuzzy as a tool
for classifying the components of the property (Effective
Factor of Property) in order to determine upon such vagueness
regarding weighted-factor. Issues of choosing algorithm
parameters are discussed on the basis of applying fuzzy
clustering to 101 metropolitan areas in the Thailand. The
result from the experiment shows that the components of the
property are weighed more appropriately and closely to the
real value which can give the percentage of reliance to be at
approximately 97 %. This enables the valuer to determine and
make a comment on property value that its evaluated value
becomes closer to the real one to the greatest extent.
Keywords-appraisal; fuzzy; real estate; effective factor of
property
I. INTRODUCTION
To perform property valuation, the valuer has always been
troubled with how to value such property. This is mostly
resulted from variation in effective factors of property such
as location, communication & access, public utility, high
test & best use and site of property. These problems have
made the valuer to take into account for analyzing and
comparing with the comparable data and led to be reluctant
in weighing each of those factors which may ultimately give
rise to inaccurately giving weight to such significant factors.
This study attempts to examine the potential of fuzzy
clustering in enriching methods for identifying housing
submarkets. Identifying housing submarkets have many
pragmatic values. It concisely reveals socio-demographic
structure of city, and helps understand geographic process
surrounding neighborhood formation. The most commonly
used method for identifying land. Clustering methods can be
roughly classified into hierarchical method and partitioning
method. Partitioning clustering can be divided into
exclusive and overlapping methods depending on which set
theory the algorithm is built on. Exclusive clustering (e.g.,
k-means) is built on classic set theory where an element is
an exclusive member of a set. Overlapping clustering is
based on fuzzy set theory where an element can be a
member of one or more sets. For instance, some aggregate
unit of housing (mainly delineated by census unit) is
composed of a mix of different housing types and diverse
demographics. In such cases, it is logical to consider that the
housing unit belongs to more than one housing submarket.
This study examines whether fuzzification offers any
advantage to methodology for land market segmentation
over counterpart based on classic set theory.
In the year 1998, models demonstrating decision
making behavior in the real estate market had been
proposed. Such models were clearly the basic model
reflecting data of real estate market which arguably made
the agent to perceive that the fastest respond in the market
would depend on the consumer’s behavior as the most
significant variable for property valuation. This was done
through model for valuation which is the program giving
accuracy and proved to give a successful result representing
the data of Edmonton, Alberta, Canada. [1]
In 2006, forecasting model, FNN, has been developed
from studying the history of relation between the reality of
buying and selling of real estate. Above all, such model
could forecast and give level of value of real estate. This
developed FNN forecasting model, from the experiment,
reflected its capability of valuing new property and its
technique is also increasingly interested by the valuer. [2]
In 2007, the study was done to categorize group of
urban residential property which is the modified procedure
influenced by membership of market of welfare housing and
was the comparison between the result of fuzzy clustering
and crisp set selecting 85 urban areas in the U.S. From such
comparison study, it was found that between fuzzy
clustering and hard clustering, the former had represented its
major characteristics to a more concise extent which was
appropriate for supporting proposed method of classification
of the housing market. [3]
In 2008, it is the year for online business of real estate
development. The market has shown its dynamic character
from 2 reasons: 1. increase of customer 2. investment in real
estate industry sector. The researcher has spent time to
emphasize on Multi-Criteria for real estate website.