Housing market segmentation and hedonic prediction accuracy

Goodman, Allen C.; Thibodeau, Thomas G.

doi:10.1016/s1051-1377(03)00031-7

Cited by 308 publications

(221 citation statements)

References 15 publications

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“…In terms of methods for addressing the problem of omitted variable bias accompanying unobservable variables, attempts have been made to estimate hedonic functions by performing market segmentation [5,6] or applying a spatial econometric method incorporating spatial autocorrelation in the error term. However, if segmenting regions, while various parameters are estimated in a more region-based form, it is difficult to obtain consistency across adjacent regions due to the segmentation of markets.…”

Section: Open Accessmentioning

confidence: 99%

Estimation of Hedonic Single-Family House Price Function Considering Neighborhood Effect Variables

Shimizu

2014

Sustainability

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Abstract:In the formulation of hedonic models, in addition to locational factors and building structures which affect the house prices, the generation of the omitted variable bias is thought to occur in cases when local environmental variables and the individual characteristics of house buyers are not taken into consideration. However, since it is difficult to obtain local environmental information in a small neighborhood unit and to observe individual characteristics of house buyers, these variables have not been sufficiently considered in previous studies. We demonstrated that non-negligible levels of omitted variable bias are generated if these variables are not considered.

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Section: Open Accessmentioning

confidence: 99%

Estimation of Hedonic Single-Family House Price Function Considering Neighborhood Effect Variables

Shimizu

2014

Sustainability

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“…Consequently, this paper also emphasizes isolation of tourists' preferences for all of these features, whereas spatial sample selection, as recently mentioned by, e.g., Boursassa et al (2003) or Goodman and Thibodeau (2003), as well as market delineation, early raised by Straszheim (1975) and Schnare and Struyk (1976), are important issues to be taken into account in order to 2 For further literature refiews see, Bourassa et al (2004). 3 These considerations should not affect hedonic equilibrium due to unobserved characteristics.…”

Section: Introductionmentioning

confidence: 86%

Valuing scenic views in coastal tourism in Italy

Amrusch

2007

TYP

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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 and hedonic prediction accuracy

Cited by 308 publications

References 15 publications

Estimation of Hedonic Single-Family House Price Function Considering Neighborhood Effect Variables

Estimation of Hedonic Single-Family House Price Function Considering Neighborhood Effect Variables

Valuing scenic views in coastal tourism in Italy

A Mass Appraisal Model Based on Market Segment Parameters

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