The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City

Özgür Özgenç; Esin Ayşe Zaimoğlu

doi:10.17798/bitlisfen.1878248

The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City

Abstract

This study presents a machine learning–based framework that integrates spatially linked real estate transaction data and crime records to predict residential property prices in the New York City (NYC) housing market over the period 2010–2024. The motivation stems from the limitation of traditional valuation approaches, which primarily emphasize structural property attributes while inadequately accounting for environmental and neighborhood-level factors. To address this gap, crime data from the NYC Police Department and real estate sales records from the NYC Department of Finance were spatially combined using Neighborhood Tabulation Areas.The resulting dataset comprises annual neighborhood-level residential sales statistics and crime data categorized into nine major types. Seven machine learning algorithms—Linear Regression, Ridge, LASSO, Random Forest, XGBoost, LightGBM, and CatBoost—were trained and evaluated using 5-fold cross-validation. Model performance was assessed using R², RMSE, MAE, and MAPE metrics.The results demonstrate that the CatBoost model achieved the highest and most stable predictive performance, with an R² value of approximately 0.75. Further analysis indicates that neighborhood characteristics are the most influential predictors of housing prices, while crime-related variables also make a meaningful contribution. Model interpretability was enhanced through SHapley Additive exPlanations (SHAP), which quantified the marginal effects of both spatial and crime-related features. Overall, the findings confirm that incorporating spatial crime information improves predictive accuracy and highlight the importance of security as a key determinant of housing prices in heterogeneous urban environments such as NYC.

Keywords

Ethical Statement

The study complies with research and publication ethics.

References

S. Gibbons, “The costs of urban property crime,” *The Economic Journal*, vol. 114, no. 499, pp. F441–F463, Nov. 2004, doi: 10.1111/j.1468-0297.2004.00254.x.
D. G. Pope and J. C. Pope, “Crime and property values: Evidence from the 1990s crime drop,” *Regional Science and Urban Economics*, vol. 42, nos. 1–2, pp. 177–188, Jan. 2012, doi: 10.1016/j.regsciurbeco.2011.08.008.
S. Rosen, “Hedonic prices and implicit markets: Product differentiation in pure competition,” *Journal of Political Economy*, vol. 82, no. 1, pp. 34–55, Jan. 1974, doi: 10.1086/260169.
V. Ceccato and M. Wilhelmsson, “Do crime hot spots affect housing prices?,” *Nordic Journal of Criminology*, vol. 21, no. 1, pp. 84–102, Jan. 2020, doi: 10.1080/2578983X.2019.1662595.
R. Thaler, “A note on the value of crime control: Evidence from the property market,” *Journal of Urban Economics*, vol. 5, no. 1, pp. 137–145, Jan. 1978, doi: 10.1016/0094-1190(78)90042-6.
S. Tarasov and B. Dessoulavy-Śliwiński, “Algorithm-driven hedonic real estate pricing—An explainable AI approach,” *Real Estate Management and Valuation*, vol. 33, no. 1, pp. 22–34, Mar. 2025, doi: 10.2478/remav-2025-0003.
P. Bayer, R. McMillan, and K. Rueben, “An equilibrium model of sorting in an urban housing market,” Cambridge, MA, USA, Nov. 2004, doi: 10.3386/w10865.
A. E. Schwartz, S. Susin, and I. Voicu, “Has falling crime driven New York City’s real estate boom?,” *Journal of Housing Research*, vol. 14, no. 1, pp. 101–135, Jan. 2003, doi: 10.1080/26911337.2003.12519486.

A. K. Lynch and D. W. Rasmussen, “Measuring the impact of crime on house prices,” *Applied Economics*, vol. 33, no. 15, pp. 1981–1989, Dec. 2001, doi: 10.1080/00036840110021735.
G. E. Tita, T. L. Petras, and R. T. Greenbaum, “Crime and residential choice: A neighborhood level analysis of the impact of crime on housing prices,” *Journal of Quantitative Criminology*, vol. 22, no. 4, pp. 299–317, Oct. 2006, doi: 10.1007/s10940-006-9013-z.
J. G. Kallberg and Y. Shimizu, “Crime measures and housing prices: An analysis using quantile regression and spatial autocorrelation,” *The Journal of Real Estate Finance and Economics*, Jan. 2025, doi: 10.1007/s11146-024-09997-w.
P. S. Morawakage, G. Earl, B. Liu, E. Roca, and A. Omura, “Housing risk and returns in submarkets with spatial dependence and heterogeneity,” *The Journal of Real Estate Finance and Economics*, vol. 67, no. 4, pp. 695–734, Nov. 2023, doi: 10.1007/s11146-021-09877-7.
C. Graif, A. S. Gladfelter, and S. A. Matthews, “Urban poverty and neighborhood effects on crime: Incorporating spatial and network perspectives,” *Sociology Compass*, vol. 8, no. 9, pp. 1140–1155, Sep. 2014, doi: 10.1111/soc4.12199.
A. Hjort, J. Pensar, I. Scheel, and D. E. Sommervoll, “House price prediction with gradient boosted trees under different loss functions,” *Journal of Property Research*, vol. 39, no. 4, pp. 338–364, Oct. 2022, doi: 10.1080/09599916.2022.2070525.
W. K. O. Ho, B.-S. Tang, and S. W. Wong, “Predicting property prices with machine learning algorithms,” *Journal of Property Research*, vol. 38, no. 1, pp. 48–70, Jan. 2021, doi: 10.1080/09599916.2020.1832558.
J. Yu, “Predicting New York housing prices: A comparative analysis of machine learning models,” in *Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy*, SCITEPRESS—Science and Technology Publications, 2024, pp. 102–109, doi: 10.5220/0012999000004601.
Department of Finance, “DOF: Summary of neighborhood sales by neighborhood citywide by borough,” *NYC Open Data*. [Online]. Available: https://data.cityofnewyork.us/City-Government/DOF-Summary-of-Neighborhood-Sales-by-Neighborhood-/5ebm-myj7/about_data. [Accessed: Jan. 7, 2026].
Department of City Planning (DCP), “2020 Neighborhood Tabulation Areas (NTAs),” *NYC Open Data*. [Online]. Available: https://data.cityofnewyork.us/City-Government/2020-Neighborhood-Tabulation-Areas-NTAs-/9nt8-h7nd/about_data. [Accessed: Jan. 7, 2026].
Police Department (NYPD), “NYPD Complaint Data Historic,” *NYC Open Data*. [Online]. Available: https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i/about_data. [Accessed: Jan. 7, 2026].
S. Koppel, J. A. Capellan, and J. Sharp, “Disentangling the impact of COVID-19: An interrupted time series analysis of crime in New York City,” *American Journal of Criminal Justice*, vol. 48, no. 2, pp. 368–394, Apr. 2023, doi: 10.1007/s12103-021-09666-1.
D. Maulud and A. M. Abdulazeez, “A review on linear regression comprehensive in machine learning,” *Journal of Applied Science and Technology Trends*, vol. 1, no. 2, pp. 140–147, Dec. 2020, doi: 10.38094/jastt1457.
A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” *Technometrics*, vol. 12, no. 1, p. 55, Feb. 1970, doi: 10.2307/1267351.
R. Tibshirani, “Regression shrinkage and selection via the lasso,” *Journal of the Royal Statistical Society: Series B (Methodological)*, vol. 58, no. 1, pp. 267–288, 1996. [Online]. Available: http://www.jstor.org/stable/2346178.
L. Breiman, “Random forests,” *Machine Learning*, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in *Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, New York, NY, USA: ACM, Aug. 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: Unbiased boosting with categorical features,” in *Advances in Neural Information Processing Systems*, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds. Curran Associates, Inc., 2018. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf.
G. Ke *et al*., “LightGBM: A highly efficient gradient boosting decision tree,” in *Advances in Neural Information Processing Systems*, 2017.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in *Advances in Neural Information Processing Systems*, 2017, doi: 10.48550/arXiv.1705.07874.
E. L. Glaeser, J. Gyourko, and R. Saks, “Why is Manhattan so expensive? Regulation and the rise in housing prices,” *Journal of Law and Economics*, vol. 48, no. 2, pp. 331–369, Oct. 2005, doi: 10.1086/429979.
Q. Truong, M. Nguyen, H. Dang, and B. Mei, “Housing price prediction via improved machine learning techniques,” *Procedia Computer Science*, vol. 174, pp. 433–442, 2020, doi: 10.1016/j.procs.2020.06.111.
U. Boryczka and M. Boryczka, “Regression models for house price analysis,” *Procedia Computer Science*, vol. 270, pp. 1091–1100, 2025, doi: 10.1016/j.procs.2025.09.230.
A. B. Adetunji, O. N. Akande, F. A. Ajala, O. Oyewo, Y. F. Akande, and G. Oluwadara, “House price prediction using random forest machine learning technique,” *Procedia Computer Science*, vol. 199, pp. 806–813, 2022, doi: 10.1016/j.procs.2022.01.100.

Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Özgür Özgenç ^*
0009-0009-1707-9257
Türkiye

Esin Ayşe Zaimoğlu
0000-0001-9688-9868
Türkiye

Publication Date

September 30, 2026

Submission Date

January 30, 2026

Acceptance Date

May 18, 2026

Published in Issue

Year 2026 Volume: 15 Number: 3

DOI

https://doi.org/10.17798/bitlisfen.1878248

IZ

https://izlik.org/JA27SJ59XE

Cite

RIS / Bibtex

APA

Özgenç, Ö., & Zaimoğlu, E. A. (2026). The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(3), 1221-1242. https://doi.org/10.17798/bitlisfen.1878248

AMA

1.Özgenç Ö, Zaimoğlu EA. The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(3):1221-1242. doi:10.17798/bitlisfen.1878248

Chicago

Özgenç, Özgür, and Esin Ayşe Zaimoğlu. 2026. “The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (3): 1221-42. https://doi.org/10.17798/bitlisfen.1878248.

EndNote

Özgenç Ö, Zaimoğlu EA (September 1, 2026) The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 3 1221–1242.

IEEE

[1]Ö. Özgenç and E. A. Zaimoğlu, “The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 3, pp. 1221–1242, Sept. 2026, doi: 10.17798/bitlisfen.1878248.

ISNAD

Özgenç, Özgür - Zaimoğlu, Esin Ayşe. “The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/3 (September 1, 2026): 1221-1242. https://doi.org/10.17798/bitlisfen.1878248.

JAMA

1.Özgenç Ö, Zaimoğlu EA. The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:1221–1242.

MLA

Özgenç, Özgür, and Esin Ayşe Zaimoğlu. “The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 3, Sept. 2026, pp. 1221-42, doi:10.17798/bitlisfen.1878248.

Vancouver

1.Özgür Özgenç, Esin Ayşe Zaimoğlu. The Impact of Crime and Spatial Features on Housing Prices: An Explainable AI Approach in New York City. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Sep. 1;15(3):1221-42. doi:10.17798/bitlisfen.1878248