Estimating the impact of crime risk on housing prices in Malaysia

• Main Authors: Woei, Chyuan Wong, Azhari, Adilah, Abdullah, Nur Adiana Hiau, Chee, Yin Yip
• Format: Article
• Published: Emerald Publishing Limited 2019
• Subjects: HG Finance
• Online Access: http://repo.uum.edu.my/27046/; http://doi.org/10.1108/IJHMA-06-2019-0063
• Institution: Universiti Utara Malaysia

Purpose The purpose of this study is to examine the impact of crime risk on housing prices at a national level in Malaysia during the period from 1988 to 2016. Design/methodology/approach A hedonic regression approach was used to estimate the Malaysian households’ valuation for crime risk. Specifically, the state-level property index on the state-level reported crime rate was regressed while controlling for state-level socioeconomic variables. The macroeconomic panel nature of the data set provides the merit to use a panel dynamic model instead of the traditional static panel data techniques (fixed effects or first difference). Findings Panel dynamic estimators consistently show a negative impact of crime risks on housing prices. The estimated elasticity of housing prices with respect to crime risks ranges from −0.141 to −0.166, in line with existing literature using micro level data. In fact, householders in crime hotspot states are willing to pay more for crime reduction compared to householders in non-hotspot states. The willingness to pay has also increased since the implementation of nationwide crime reduction plans in 2010. Research limitations/implications This is the first study that has examined the Malaysian people’s willingness to pay to reduce crime. This information is important in determining the optimal level of government expenditures for public safety. Originality/value This is the first study to examine the relationship between crime rates and housing prices in Malaysia. This study contributes to the literature by examining the impact of crime rates on housing prices at a national level by using panel dynamic models. The macro level data results are consistent and complement the existing literature based on micro level data.