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Activity Number: 539 - SPEED: Bayesian Methods and Applications in the Life and Social Sciences
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #332789
Title: Dirichlet Process Clustering for the Prediction of Housing Prices
Author(s): Matt Slifko* and Scotland Leman and David Bieri
Companies: Virginia Tech and Virginia Tech and Virginia Tech
Keywords: clustering; Dirichlet process; outlier detection; hedonic regression; property values

Purchasing a house is one of the most expensive and long-term investments that most people will make in their lifetime. The ability to accurately estimate property values is of importance to homebuyers, homeowners, lending companies, insurance companies, tax assessors, and policymakers. In recent years, websites, such as Zillow, Trulia, and Realtor, have popularized the use of Automated Valuation Models (AVMs). AVMs utilize the characteristics of a property to provide a quick and relatively inexpensive estimate of the property's value. Unfortunately, the data used to train AVMs are often incomplete and filled with inaccuracies. We present a method employing a Dirichlet process for clustering properties in the presence of outliers.

Authors who are presenting talks have a * after their name.

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