Housing Market Segmentation

Goodman, Allen C.; Thibodeau, Thomas G.

doi:10.1006/jhec.1998.0229

Cited by 367 publications

(250 citation statements)

References 11 publications

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“…Earlier studies show a significant positive correlation between the three major factors characterizing the residential area's socioeconomic level -income, education and employment -and the price of dwellings (Heikkila 1992;Potepan 1996;Goodman and Thibodeau 1998;Greenberg 1999;Des Rosiers et al 2002;Yates 2002). Income is considered the primary factor (Ozanne and Thibodeau 1983;Malpezzi et al 1998).…”

Section: Dwelling Price In Light Of Socioeconomic Level Characteristicsmentioning

confidence: 99%

Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation

Fleishman¹,

Gubman²,

Tur‐Sinai³

2015

Journal of Official Statistics

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In order to characterize the socioeconomic profile of various geographic units, it is common practice to use aggregated indices. However, the process of calculating such indices requires a wide variety of variables from various data sources available concurrently. Using a number of administrative databases for 2001 and 2003, this study examines the question of whether dwelling prices in a given locality can serve as a proxy for its socioeconomic level. Based on statistical and geographic criteria, we developed a Dwelling Price Ranking (DPR) methodology. Our findings show that the DPR can serve as a good approximation for the socioeconomic cluster (SEC) calculated by the Israel Central Bureau of Statistics for years when the required data was available. As opposed to the SEC, the suggested DPR indicator can easily be calculated, thus ensuring a continuum of socioeconomic index series. Both parametric and nonparametric statistical analyses have been carried out in order to examine the additional social, demographic, location, crime and security effects that are exogenous to SEC. Complementary analysis on recently published SEC series for 2006 and 2008 show that our conclusions remain valid. The proposed methodology and the obtained findings may be applicable for different statistical purposes in other countries which possess dwelling transactions data.

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Section: Dwelling Price In Light Of Socioeconomic Level Characteristicsmentioning

confidence: 99%

Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation

Fleishman¹,

Gubman²,

Tur‐Sinai³

2015

Journal of Official Statistics

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“…Later studies began questioning the validity of the assumption of long-run equilibrium. Large variety in structural and locational attributes of houses, immobility of capital, inelasticity of housing supply and time-dependent changes in supplier and buyer behavior all suggest that the housing market is not uniform but rather consists of distinct submarkets (Goodman and Thibodeau, 1998). Dwellings that are seen by buyers as close substitutes for one another, but unlikely alternatives for others form a submarket.…”

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confidence: 99%

Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes

Ayan¹,

Erkin²

2014

IJEF

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The share of apartment complexes in housing stock of major cities in the developing world has been increasing. They represent a different housing submarket whose growing importance necessitates further research. Using survey data on transaction records of real estate agents, we employ a hedonic pricing model to explore the factors influencing apartment prices in the Metropolitan Izmit area. The site of a major reconstruction effort following a strong earthquake, Izmit has been in the forefront of the changes taking place in the Turkish real estate sector in particular, and in urban housing in general. We aim to get better estimates by focusing the study on apartment complexes in Metropolitan Izmit, the major submarket in the area. The results reveal that structural characteristics of an apartment as well as amenities provided in an apartment complex are important determinants of price. Air quality and proximity to a good public school have a major effect on price as well. The findings suggest that the submarket could be segmented further geographically.

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“…To improve the valuation accuracy other authors, consider spatial information in pricing models using the direct spatial modelling with Cartesian coordinates [17,18], geostatistical models [19], or response surfaces [20][21][22]. Other research [23][24][25][26] has focused on submarkets in which the marginal price contributions of independent variables are more likely to be similar. Quintos in 2013 [27] used Spatial Lag Models to create location based base prices and location adjustment factors.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

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Abstract:The proposed evaluation scheme is a uni-equation model to evaluate properties of Mass Appraisal (MA) in terms of widespread availability of sample data. It all allows the use of statistical models and in the opposite conditions of the absence of data of comparable properties, the functions of similar market areas are known as well as the ones near to those for which you want to estimate the function. Of course, the accuracy of the evaluation increases with the amount of available data, with other equal conditions and evaluations carried out without data (but in the presence of other market information). It requires extra-statistical appraisal procedures involving a complete knowledge of the real estate market. However, such knowledge is also required in the MA performed by quantitative models with regard to the data sampling and performance monitoring process. The model considers micro-level characteristics of the properties and macro-level parameters of the real estate market segments. The appraisal model defines the prediction function with both the statistical models and estimation procedures. For this purpose, the model considers four specific situations: the construction of a statistical model operating with a sufficiently large sample of market prices; the construction of a prediction function operating with a very few number of market prices samples; in this situation, the appraisal function of market value is defined by using a sample of market prices referred to comparable properties, and these are few for statistical use but perfectly suitable to the appraisal process; the construction of a prediction function operating with only one market price; the construction of a prediction function operating in the absence of real estate data but with similar functions of market areas with other estimated proprieties. The presented model provides a uniform method of estimating the market value of properties (and fees), through the modular functions. The model studied is able to operate also with reduced information, considering the practical circumstances, the boundary conditions, the application precautions and the significance of the results.

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Housing Market Segmentation

Cited by 367 publications

References 11 publications

Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation

Dwelling Price Ranking versus Socioeconomic Clustering: Possibility of Imputation

Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes

A Mass Appraisal Model Based on Market Segment Parameters

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