Influence of the Cadastral Value of the Urban Land and Neighborhood Characteristics on the Mean House Mortgage Appraisal

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.

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

“…In Spain's post-crisis period, namely from 2015 to 2019, Valencia was one of the cities that, despite losing population during the economic recession, regained it, becoming an urban area in which the periphery has grown more than the city core, with predominant suburbanization [49]. According to recent research, Valencia's central area (which includes the historic center and Ensanchesee Figure 1) and its surrounding districts, were the areas where prices per square meter of residential properties were higher, while the lowest prices were mainly in the north and south-west peripheral areas [50].…”