Combining Geographic Information Systems and Regression Models to Generate Locational Value Residual Surfaces in the Assessment of Residential Property Values

Hamid, Aidil Abdul

doi:10.1080/14445921.2007.11104222

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In particular, our study is the first that applies GIS to model, construct and display of the neighbourhoodlevel HPI for reasons previously mentioned. Nevertheless, the past studies that have been conducted to use surface responses to analyse capital values or prices of properties form the basis for spatial price indexation (Hamid, 2007;LaRose, 1988;McCluskey et al, 1997). The basic principle of this indexation is mapping spatial changes of relative property prices over a geographic space to aid in spatial analyses and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Spatial–temporal neighbourhood-level house price index

Sipan

Iman

Razali

2018

IJHMA

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Purpose-The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system. Design/methodology/approach-By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study. Findings-The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood. Research limitations/implications-This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems. Originality/value-Neighbourhood-level HPI is needed for a better understanding of the local housing market.

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Section: Introductionmentioning

confidence: 99%

Spatial–temporal neighbourhood-level house price index

Sipan

Iman

Razali

2018

IJHMA

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“…A priori, variables como la zona ajardinada tienen una escasa vinculación con el precio (3%), algo que puede venir explicada inicialmente por la escasa representación en la muestra de las viviendas con esta característica. Debe señalarse que en esta matriz no aparece la variable antigüedad por ser categórica, pese a su previsible correlación significativa con el precio; ni tampoco ninguna de las variables ligadas a la ubicación, una de las características más referidas en la literatura (Pearson, 1991;Hamid, 2007;Kucklick y Müller, 2020).…”

Section: Figura 1 Diagrama De Barras De La Variable Antigüedadunclassified

Valoración automática de inmuebles residenciales mediante modelos de Machine Learning

Guijarro¹

2023

ReE

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La literatura reciente sobre valoración inmobiliaria ha aportado diversas evidencias en el ámbito internacional sobre el buen desempeño de los modelos de machine learning en la predicción del comportamiento de los precios, sobre todo si se comparan con los obtenidos por los denominados métodos tradicionales valoración, muy extendidos sobre todo en la práctica profesional. Con todo, se siguen remarcando algunas limitaciones como el diseño de caja negra y la dificultad en la interpretación de los resultados proporcionados por estas técnicas. Este trabajo tiene por objeto comparar los resultados y el desempeño de diferentes modelos de machine learning aplicados en el ámbito de la valoración inmobiliaria residencial. Para ello se ha recopilado una amplia base de datos con ofertas de inmuebles en la ciudad de Madrid, que permite dividir la muestra en los grupos de entrenamiento y test. La comparación entre los modelos se ha llevado a cabo a través de diferentes métricas, entre las que destaca el MAPE (Mean Absolute Percentage Error) por ser uno de los preferidos por las sociedades de tasación. Las métricas utilizadas confirman un buen rendimiento generalizado para el conjunto de modelos entrenados, con variaciones relativamente pequeñas tras el proceso de validación.

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Spatial variation as a determinant of house price

McCord

Davis

Haran

et al. 2012

Journal of Financial Management of Property and Construction

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Purpose -Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach. Design/methodology/approach -The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1-Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area. Findings -This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market. Originality/value -This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

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Combining Geographic Information Systems and Regression Models to Generate Locational Value Residual Surfaces in the Assessment of Residential Property Values

Abstract: ABSTRACT

Cited by 3 publications

References 20 publications

Spatial–temporal neighbourhood-level house price index

Spatial–temporal neighbourhood-level house price index

Valoración automática de inmuebles residenciales mediante modelos de Machine Learning

Spatial variation as a determinant of house price

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