Property Price Modelling, Market Segmentation and Submarket Classifications: A Review

Usman, Hamza; Lizam, Mohd; Adekunle, Muhammad Usman

doi:10.1515/remav-2020-0021

Cited by 21 publications

(16 citation statements)

References 48 publications

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“…Some research has provided evidence that segmenting property market often improves mass valuation [41]. A procedure of determining submarkets has been introduced in model (1) as well.…”

Section: Methodsmentioning

confidence: 99%

Property Mass Valuation on Small Markets

Gnat¹

2021

Land

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The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon.

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“…Some research has provided evidence that segmenting property market often improves mass valuation [41]. A procedure of determining submarkets has been introduced in model (1) as well.…”

Section: Methodsmentioning

confidence: 99%

Property Mass Valuation on Small Markets

Gnat¹

2021

Land

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“…In local markets, spatial variability is caused by such local factors as spatial and planning determinants as well as localisation aspects associated with the temporary trends, preferences, safety, and the image of a particular estate or district [46]. As Usman et al [47] noted, a single method for segmenting a property market into submarkets that are internally homogeneous and heterogeneous among the submarkets is yet to be proposed and generally accepted. The property market is generally subdivided into two classes.…”

Section: Stage 3: Assessment Of the Effect Of Flood Hazard Areas On T...mentioning

confidence: 99%

The Impact of Flood Risk on the Activity of the Residential Land Market in a Polish Cultural Heritage Town

et al. 2020

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The article attempts to determine the effect of perceived flood risk, based on identified flood hazard zones, on the level of activity in the market of land property designated for housing developments in the historical town of Sandomierz, Poland. The study employed graphical, analytical, quantitative methods, and spatial analyses with GIS tools. The proposed methodology, involving spatial interpolation of the phenomenon (Kernel Density Estimation (KDE) and Inverse Distance Weighting (IDW)) and an expert opinion survey, facilitates the assessment of the market activity in towns where transactions are scarce. Trade in property is lower in areas at risk of flooding than for the remaining parts of the town. The potential flood hazard zone affects both the activity of the property market and the average prices of land. The study demonstrated that both a flood and flood risk affect the levels of market activity and the prices of residential land. However, this impact differs at various times and locations and is greater immediately after a flood. Properties located in the most attractive location within an area are characterised by a greater sensitivity to this risk.

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“…Kokot (2020) analyzed the influence of socioeconomic factors on housing prices in Poland and proposed a City Wealth Synthetic Measure. Usman et al (2020) conducted an extensive literature analysis of property market segmentation into submarkets to improve the analysis of real estate prices. The publication mainly describes 3 types of methods to segment the market, i.e.…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

Gružauskas

Čalnerytė

Fyleris

et al. 2021

Real Estate Management and Valuation

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The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.

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Property Price Modelling, Market Segmentation and Submarket Classifications: A Review

Cited by 21 publications

References 48 publications

Property Mass Valuation on Small Markets

Property Mass Valuation on Small Markets

The Impact of Flood Risk on the Activity of the Residential Land Market in a Polish Cultural Heritage Town

Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

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