Do crime hot spots affect housing prices?

Ceccato, Vânia; Wilhelmsson, Mats

doi:10.1080/2578983x.2019.1662595

Cited by 31 publications

(26 citation statements)

References 51 publications

(68 reference statements)

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“…Some studies have found this to be the case. For example, Lynch and Rasmussen (2001) find that for Jacksonville as a whole crime had virtually no effect on house prices overall, while Ceccato and Wilhelmsson (2020) reached the same conclusion for Stockholm. Gibbons (2004) finds that for London as a whole, criminal damage lowers housing prices, while burglary and housing prices are not related.…”

Section: Why Should Income Inequality Affect House Prices?mentioning

confidence: 82%

“…The pricing impact of crime is complex and varies by type of crime, property type, and location (McIlhatton et al, 2016). Most of the literature that studies the effect of crime on house prices is about local areas and finds that crime leads to a decrease of house prices in crime hotspots -less demand-and an increase in house prices in neighboring areas, which tend to see an inflow of residents that leave crime hotspots (Ceccato & Wilhelmsson, 2020).…”

Section: Why Should Income Inequality Affect House Prices?mentioning

confidence: 99%

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Income inequality and housing prices in the very long‐run

Hailemariam

Churchill

Smyth

et al. 2021

Southern Economic Journal

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We examine the relationship between income inequality and house prices for a panel of 17 OECD countries over the period 1870 to 2015. Our identification strategy takes advantage of exogenous variation in culturally weighted communist influence to instrument for within-country variations in income inequality. Controlling for endogeneity, time, and country fixed effects, our results suggest that an increase in income inequality has a significant negative effect on real house prices. This finding is robust to the use of both the Gini coefficient and top income share as measures of income inequality and the use of absolute and relative income inequality measures, as well as a range of other robustness checks. We examine crime as a mechanism through which income inequality influences housing prices and find that the theft rate is a channel via which higher income inequality contributes to lower house prices.

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Section: Why Should Income Inequality Affect House Prices?mentioning

confidence: 82%

Section: Why Should Income Inequality Affect House Prices?mentioning

confidence: 99%

Income inequality and housing prices in the very long‐run

Hailemariam

Churchill

Smyth

et al. 2021

Southern Economic Journal

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“…Kim & Kim, 2020;Leinberger & Alfonzo, 2012), greenery (Ye et al, 2019;Y. Zhang & Dong, 2018), openness (Chen et al, 2020;Fu et al, 2019), safety (Ceccato & Wilhelmsson, 2020;Naik et al, 2014), aesthetics (Cetintahra & Cubukcu, 2015;Jim & Chen, 2009;Quercia et al, 2014), and risks (McCluskey & Rausser, 2001) in a neighborhood all have correlations with residents' everyday behaviors, psychological perceptions of the place, wiliness to pay, and eventually the surrounding property values.…”

Section: Selection Of the Six Perceptual Qualitiesmentioning

confidence: 99%

“…Zhang et al, 2018). Prior studies implied that in districts perceived as being less safe from surveys (Buonanno et al, 2013) or with higher crime rates (Ceccato & Wilhelmsson, 2020), houses are highly discounted. However, how perceived safety from street appearance will affect house prices has not been clearly stated in literature.…”

Section: Selection Of the Six Perceptual Qualitiesmentioning

confidence: 99%

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Qiu¹,

Li²,

Zhang³

et al. 2021

Preprint

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The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

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“…Crime concentration is a key element for designing and measuring crime reduction strategies (Rogerson 2008 ), accounting for the public health burden (Carter et al. 2019 ) and detecting the impacts of crime on the real estate market (Ceccato and Wilhelmsson 2020 ) among many. The fact that crime concentrates among some individuals or is perpetrated only by a limited number of people also impacts prevention programs and is a crucial driver of fear of crime (Prieto Curiel and Bishop 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Weekly Crime Concentration

Curiel

2021

J Quant Criminol

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Objectives Examine and visualise the temporal concentration of different crime types and detect if their intensity varies through distinct moments of the week. Methods The “heartbeat of the crime signal” is constructed by overlapping the weekly time they were suffered. This study is based on more than 220,000 crimes reported to the Mexico City Police Department between January 2016 and March 2020 to capture the day and time of crimes and detect moments of the week in which the intensity exceeds the average frequency. A new metric for the temporal concentration of crime is constructed for different types of crime and regions of the city based on the corresponding heartbeats. Results The temporal concentration of crime is a stable signature of different types of crime. The intensity of robberies and theft is more homogeneous from Monday to Sunday, but robberies of a bank user are highly concentrated in a week, meaning that few hours of the week capture most of the burning moments. The concentration is not homogeneously distributed in the city, with some regions experiencing a much higher temporal concentration of crime. Conclusions Crime is highly concentrated when observed in its weekly patterns, but different types of crime and regions exhibit substantially distinct concentration levels. The temporal trace indicates specific moments for the burning times of different types of crime, which is a critical element of a policing strategy.

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Do crime hot spots affect housing prices?

Cited by 31 publications

References 51 publications

Income inequality and housing prices in the very long‐run

Income inequality and housing prices in the very long‐run

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Weekly Crime Concentration

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