Determining the Impact of Residential Neighbourhood Crime on Housing Investment Using Logistic Regression
This paper discusses the impact of criminal activities on residential property value. With regard to criminal activities, the paper emphasizes on the contribution of each component of property crime. One thousand (1000) sets of structured questionnaire were administered to the residents of residential estates within the South Western States of Nigeria out of which 467 were considered useable after the data screening. Purposive and systematic sampling techniques were used while logistic regression was used to determine the impact of each of the components of residential property crime on housing investment. The results showed the P-Values of 0.000, 0.322, 0.335, 0.545 and 0.992 for violent crime, incivilities and street crime, burglary and theft, vandalism and robbery respectively. However, the R2 which represents the generalisation of the impact of neighbourhood crime on housing investment was 44% and aggregate P-value was 0.000. Using the Hosmer and Lemeshow (H-L) test of goodness of fit, the model had approximately 89% predictive probability which is considered excellent. This indicates that the alternative hypothesis is upheld that residential neighbourhood crime is capable of impacting on residential property value. The policy implication of this result is that no effort should be spared in combating residential neighbourhood crime in order to boost and encourage housing investment.
1. Agunbiade, M. E. (2012). Land Administration for Housing Production (Doctoral thesis, University of Melbourne). Retrieved from http://csdila.unimelb.edu.au/publication/journals/ts05b_agunbiade_rajabifard_et_al_4809.pdf
2. Austin, J. T., Yaffee, R. A., & Hinkle, D. E. (1992). Logistic regression for research in higher education. Higher Education: Handbook of Theory and Research, 8, 379–410.
3. Boggess, L. N., Greenbaumb, R. T., & Tita, G. E. (2013). Does crime drive housing sales? Evidence from Los Angeles. Journal of Crime and Justice, 36(3), 299-318. doi: 10.1080/0735648X.2013.812976
4. Buonanno, P., Montolio, D., & Raya-Vílchez, J. M. (2013). Housing Prices and Crime Perception, Empirical Economics, 45(1), 305-321. doi: 10.1007/s00181-012-0624-y
5. Cabrera, A. F. (1994). Logistic regression analysis in higher education: An applied perspective. Higher Education: Handbook of Theory and Research, 10, 225-256.
6. Shapiro R., & Hassett, K. (2012). The Economic Benefits of Reducing Violent Crime. Retrieved from https://cdn.americanprogress.org/wp-content/uploads/issues/2012/06/pdf/violent_crime.pdf
7. Chuang, H. L. (1997). High school youth’s dropout and re-enrollment behavior. Economics of Education Review, 16(2), 171-186. doi: 10.1016/S0272-7757(96)00058-1
8. Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London: Chapman and Hall.
9. Cozens, P. (2014). Think crime! Using evidence, theory and crime prevention through environmental design (CPTED) for planning safer cities. Quinns Rocks: Paxis Education.
10. Cozens, P., & Love, T. (2015). A review and current status of crime prevention through environmental design (CPTED). Journal of Planning Literature, 30(4), 393–412. doi: 10.1177/0885412215595440
11. Cozens, P., & Melenhorst, P. (2015). Exploring community perceptions of crime and crime prevention through environmental design (CPTED) in Botswana. In Papers from the British Criminology Conference (Vol. 14, pp. 65–83). Retrieved from http://britsoccrim.org/new/volume14/pbcc_2014_cozens.pdf
12. Crowe, T. D., National Crime Prevention Institute (University of Louisville). (1991). Crime prevention through environmental design: Applications of architectural design and space management concepts. Boston: Butterworth-Heinemann.
13. Eck, J. E. (1993). The threat of crime displacement. Criminal Justice Abstracts, 25(3), 527-546. Retrieved from http://www.popcenter.org/library/psq/1993/Summer_1993_Vol_6_No_3.pdf
14. Ekblom, P., Armitage, R., Monchuk, L., & Castell, B. (2013). Crime prevention through environmental design in the United Arab Emirates: a suitable case for reorientation? Built Environment, 39(1), 92-113. doi: 10.2148/benv.39.1.92
15. Frischtak, C., & Mandel, B. R. (2012, January 1). Crime, House Prices, and Inequality: The Effect of UPPs in Rio. doi: 10.2139/ssrn.1995795
16. Gibbons, S. (2004). The Costs of Urban Property Crime*. The Economic Journal, 114(499), F441-F463. doi: 10.1111/j.1468-0297.2004.00254.x
17. Grabosky, P. (1995). Burglary prevention. Trends & Issues in Crime and Criminal Justice, 49. Retrieved from http://aic.gov.au/media_library/publications/tandi_pdf/tandi049.pdf
18. Greenbaum, R. T., & Tita, G. E. (2004). The impact of violence surges on neighbourhood business activity. Urban Studies, 41(13), 2495-2514. doi: 10.1080/0042098042000294538
19. Hastings, R. (2008). Prévenir le crime : réduire la criminalité et accroître la sécurité dans un Canada ouvert à tous [Achieving Crime Prevention: Reducing Crime and increasing Security in an inclusive Canada]. Ottawa: University of Ottawa (in French).
20. Hosmer, D. W., & Lemeshow, S. (2000). Introduction to the logistic regression model. In Applied Logistic Regression (2nd ed.) (pp. 1-30). doi: 10.1002/9781118548387.ch1
21. Ihlanfeldt, K., & Mayock, T. (2010). Crime and housing prices. In B. Benson, P. Zimmerman, Handbook on the economics of crime (pp. 303-327). Cheltenham Glos: Edward Elgar Publishing.
22. Lei, P.-W., & Koehly, L. M. (2003). Linear discriminant analysis versus logistic regression: A comparison of classification errors in the Two-Group Case. The Journal of Experimental Education, 72(1), 25–49. doi: 10.1080/00220970309600878
23. Long, J. S. (1997). Regression models for categorical and limited dependent variables. London: Sage.
24. Linden, L., & Rockoff, J. E. (2008). Estimates of the impact of crime risk on property values from Megan's laws. The American Economic Review, 98(3), 1103-1127. doi: 10.1257/aer.98.3.1103
25. Lynch, A. K., & Rasmussen, D. W. (2001). Measuring the impact of crime on house prices. Applied Economics, 33(15), 1981–1989. doi: 10.1080/00036840110021735
26. Maximino, M. (2014, March 12). The impact of crime on property values: Research roundup. Retrieved from https://journalistsresource.org/studies/economics/real-estate/the-impact-of-crime-on-property-values-research-roundup
27. Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. The American Statistician, 54(1), 17–24. doi: 10.2307/2685605
28. Moreto, W. (2010). Risk factors of urban residential burglary. RTM Insights, 4, 1–3. Retrieved from http://www.rutgerscps.org/uploads/2/7/3/7/27370595/burglaryrisks.pdf
29. Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692. doi: 10.2307/2337038
30. Olajide, S, E. & Lizam, M. (2016). Residential neighbourhood Security challenges: Assessing crime prevention concepts and Techniques. International Journal of Academic Research and Development, 1(9), 7–15.
31. Olajide, S. E., Lizam, M., & Adewole, A. (2015). Towards a crime-Free Housing: CPTED versus CPSD. Journal of Environment and Earth Science, 5(18), 53–63. Retrieved from http://www.iiste.org/Journals/index.php/JEES/article/view/26022/26570
32. Owusu, G., Wrigley-Asante, C., Oteng-Ababio, M., & Owusu, A. Y. (2015). Crime prevention through environmental design (CPTED) and built-environmental manifestations in Accra and Kumasi, Ghana. Crime Prevention & Community Safety, 17(4), 249-269. doi: 10.1057/cpcs.2015.8
33. Pallant, J. (2011). SPSS Survival Manual. A step by step guide to data analysis using the SPSS program (4th ed.). Sydney: Allen & Unwin.
34. Peng, C. J. & Lee, K. L. & Ingersoll, G. M. (2002). An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1), 3–14. doi: 10.1080/00220670209598786.
35. Ratcliffe, J. (2001). Policing urban burglary. Trends & Issues in Crime and Criminal Justice, 213. Retrieved from http://www.aic.gov.au/media_library/publications/tandi_pdf/tandi213.pdf
36. Sutton, A., Cherney, A., & White, R. (2013). Crime prevention: principles, perspectives and practices. Cambridge: Cambridge University Press.
37. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.
38. Tita, G. E., Petras, T. L., & Greenbaum, R. T. (2006). Crime and Residential Choice: A Neighborhood Level Analysis of the Impact of Crime on Housing Prices. Journal of Quantitative Criminology, 22(4), 299–317. doi: 10.1007/s10940-006-9013-z
39. Tolman, R. M., & Weisz, A. (1995). Coordinated community intervention for domestic violence: The effects of arrest and prosecution on recidivism of woman abuse perpetrators. Crime and Delinquency, 41(4), 481–495. doi: 10.1177/0011128795041004007
40. The John Howard Society of Alberta. (1995). Crime Prevention through Social Development: A Literature Review. Retrieved from http://www.johnhoward.ab.ca/pub/old/pdf/C6.pdf
41. Troy, A., & Grove, J. M. (2008). Property values, parks, and crime: A hedonic analysis in Baltimore, MD. Landscape and urban planning, 87(3), 233–245. doi: 10.1016/j.landurbplan.2008.06.005
42. Waller, I., & Weiler, D. (1985). Crime prevention through social development. Ottawa: Canadian Council on Social Development. Retrieved from https://www.ncjrs.gov/pdffiles1/Digitization/103113NCJRS.pdf
Metrics powered by PLOS ALM
- There are currently no refbacks.
Copyright (c) 2016 S. E. Olajide, M. Lizam
This work is licensed under a Creative Commons Attribution 4.0 International License.