Skip to main content

Predicting Housing Abandonment with the Philadelphia Neighborhood Information System

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Abstract

Several large US cities, including Chicago, Los Angeles, New York, and Philadelphia, have developed information systems to distribute property‐level housing data to community organizations and municipal agencies. These early warning systems are also intended to predict which properties are at greatest risk of abandonment, but they have rarely used statistical modeling to support such forecasts. This study used logistic regression to analyze data from the Philadelphia Neighborhood Information System in order to determine which properties were most likely to become imminently dangerous. Several different characteristics of the property, including whether it was vacant, had outstanding housing code violations, and tax arrearages as well as characteristics of nearby properties were identified as significant predictors. Challenges common to the development of early warning systems—including integrating administrative data, defining abandonment, and modeling temporal and spatial data—are discussed along with policy implications for cities like Philadelphia that have thousands of vacant and abandoned properties.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-9906.00007

Affiliations: University of Pennsylvania

Publication date: February 1, 2003

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Partial Open Access Content
Partial Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more