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The aim of this paper is to analyse the relationship of crime on housing prices during the economic recovery of the housing sector in Spain (2014–2019).

For this purpose, it is used a regional panel data in first differences for the period 2014–2019 in order to control the endogeneity and multicollinearity that these models usually present in the empirical literature.

The results show that it takes between one and two years for housing prices in Spain to respond negatively to an increase in crime. Of the eight types of crime analysed, only four of them establish statistically significant relationships with housing prices, while robbery with violence (−2.2%) and burglary with forced entry (−0.5%) have the greatest negative impact on housing prices. Lastly, the results highlight the fact that the category “other crimes against property” – which includes squatting – is the only crime typology that has an immediate effect on housing prices (−1.7%).

These results demonstrate that the more serious property crimes have a greater quantitative negative impact on housing prices. These crimes combine both a violent nature and the fact that they affect property, thus generally causing greater harm to individuals’ emotional well-being and perception of criminality. These findings have direct implications for crime prevention strategies, as the housing market in Spain appears to be more affected by this type of criminal activity. Consequently, public institutions should focus their efforts on mitigating these crimes.

This is the first study that examines the role of crime in the recovery of the real estate sector in Spain following the economic crisis of 2008. It is also one of the pioneering works for the European context, utilizing a panel data approach with first differences and incorporating various types of criminal activities within the same model.

This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used.

Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data.

Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district (p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the local market or reporting inconsistencies, demand further investigation. Variables such as views, proximity to wetlands, roads, green areas, religious institutions and schools were statistically insignificant across both data sets (p > 0.05).

It provides a robust basis for housing and urban development strategies. The stakeholders such as real estate developers, urban planners and policymakers are encouraged to incorporate these findings into housing policies, land value capture initiatives and urban planning frameworks to enhance residential property value and align with sustainable urban development goals.

This study contributes original insights into single-family residential property valuation by integrating willingness-to-pay and transaction data, substantiating the determinants of property value.