Analysis of the Correlation Between Crime Rate and Housing Price in Washington, D.C., USA Based on Big Data

DOI

10.2991/978-94-6463-034-3_120How to use a DOI?

Keywords

Crime rate; house price; random forest; linear regression; XGBoost

Abstract

Knowledge of what happens to housing values is limited when properties are near high crime density areas. Big data analysis has become one of the tools for effective crime prevention and can be used as an effective reference when buying house. In this article, we analyzed crime data from 2017 to 2021 in Washington, D.C., and a data set of housing sales information in Washington, D.C. in 2018, which includes crime rates for nine different crime types, as well as internal and external information. The configuration of sold houses in the DC area uses a naive Bayes model to predict the ward where the next crime will occur, and uses XGBoost to explore the housing characteristics of the housing price. The results show that the crime rate of burglary is the highest among all crime types, while the crime rate of ward2 is the highest and the housing price is relatively low. We also created a multiple regression model to predict housing prices based on many numerical and categorical variables provided by the data set. After several cycles of processing and optimization, the most useful parameters for predicting the sales price of houses are determined as the forecasting tool for future housing prices. The results show that the three areas with the highest housing prices are Southwest First Street/Canal Street, Southwest Third Street/Southwest D Street, and Rhode Island Avenue Northwest/Northwest 8th Street. In addition, the regional crime rate is also related to housing prices.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hanshu Yang
AU  - Meili Liu
AU  - Jeng-Eng Lin
AU  - Chun-Te Lee
PY  - 2022
DA  - 2022/12/23
TI  - Analysis of the Correlation Between Crime Rate and Housing Price in Washington, D.C., USA Based on Big Data
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 1165
EP  - 1175
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_120
DO  - 10.2991/978-94-6463-034-3_120
ID  - Yang2022
ER  -