Now You See it, Now You Don't: Why Do Real Estate Agents Withhold Available Houses from Black Customers?

Potential home buyers may initiate contact with a real estate agent by asking to see a particular advertised house. This paper asks whether an agent's response to such a request depends on the race of the potential buyer or on whether the house is located in an integrated neighborhood. We build on previous research about the causes of discrimination in housing by using data from fair housing audits, a matched-pair technique for comparing the treatment of equllay qualified black and white home buyers. However, we shift the focus from differences in the treatment of paired buyers to agent decisions concerning an individual housing unit using a sample of all houses seen during he 1989 Housing Discrimination study. We estimate a random effect, multinomial logit model to explain a real estate agent's joint decisions concerning whether to show each unit to a black auditor and to a white auditor. We find evidence that agents withhold houses in suburban, integrated neighborhoods from all customers (redlining), that agents' decisions to show houses in integrated neighborhoods are not the same for black and white customers (steering), and that the houses agents show are more likely to deviate from the initial request when the customeris black than when the customer is white. These deviations are consistent with the possibility that agents act upon the belief that some types of transactions are relatively unlikely for black customers (statistical discrimination).

Evaluation of Police Patrol Patterns

This paper describes an ArcView extension that allows police planners to design patrol districts and to evaluate them by displaying various performance measures. It uses a spatially distributed queuing system (the Larson Hypercube) to calculate expected travel times, workloads, preventive patrol frequencies, and other variables; and it allows planners to see the unavoidable tradeoffs among their objectives. Using this tool, planners can experiment with various patrol patterns to find those that best meet their Department.s goals. For example, those patrol patterns which are best in terms of average response time don.t do as well as others in terms of workload balance, or those that are best in terms of achieving a uniform response time across different parts of the city don't do as well as others in terms of minimizing inter-district dispatches. There is, of course, no perfect solution for this problem: the facts of the situation force us to balance competing goals. Described here is a way of explicitly weighting the alternative objectives.