Abstract
As house mortgage appraisal values have played a leading role in the 2007–2012 financial crisis, it is important to develop robust mass appraisal models that correctly estimate these values. The present paper intends to propose a methodology to examine the spatial distribution of house mortgage appraisal values. To do so, we analyzed the effect that these values, cadastral urban land values, characteristics of houses, and socioeconomic conditions and services in neighborhoods, have on house mortgage appraisal values in the 70 boroughs of Valencia (Spain). Econometric and spatial models were used, and variables were calculated as the mean and weighted values per boroughs. Our results showed that the hierarchy of cadastral values impacted mortgage appraisal values. Conversely, not all the boroughs-related variables influenced the mean mortgage values of houses, although some did anomalously. We conclude that the spatial error or autoregressive models provided very good fit results, which somewhat improved the ordinary least square model. Moreover, house mortgage appraisal values may be influenced by not only cadastral values but also by some district characteristics like mean family property size, vehicle age, distance from a metro station or from infant or primary education centers.
Highlights
Numerous studies have been conducted to model house prices, and quite often to use them in mass appraisals [1,2]
This study aimed to propose a methodology to examine the spatial distribution of house mortgage appraisal values
The ordinary least square (OLS) and the spatial error, or autoregressive models, provided very good fit results, which somewhat improve when spatial aspects are added and, in turn, eliminate the spatial autocorrelations observed in the OLS model
Summary
Numerous studies have been conducted to model house prices, and quite often to use them in mass appraisals [1,2]. In the aftermath of the recent boom and the real-estate burst in developed countries, growing interest in appraisal methods has been shown [8] with extended use of spatial econometrics. This has been possible thanks to the development of geographic information systems (GIS), which have proven most useful in mortgage financing [9]. Autoregressive models have recently progressed to more sophisticated models [12,13,14,15] with different weight matrices [16,17], which may lead to distinct results [11]
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