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Analysis of American Housing Survey II Data for Hedonic Equation Estimation

This project involves analyzing American Housing Survey II data to estimate a hedonic equation for house characteristics. The dataset contains 103 variables on 21,720 observations of owner-occupied homes in certain metropolitan areas. By examining the housing characteristics such as bedrooms, bathrooms, kitchen features, and more, the goal is to understand how these factors influence the value of a house. Using regression analysis (OLS), the study will explore the relationship between house characteristics, size of the property, and pricing factors. The analysis will also consider the functional form of the data and assess the contribution of each variable to the overall value. This research aims to provide insights into the factors affecting housing prices in American metropolitan areas.

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Analysis of American Housing Survey II Data for Hedonic Equation Estimation

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  1. American Housing Survey II

  2. Data • Model drives variable selection • Selection Criteria • Certain MSAs • Owners only • Known metro status • Known year built • 103 variables on 21720 observations

  3. Data Download • Go to http://research.umbc.edu/~coates/ec611/owner_housing.zip • Unzip the file using Winzip • Read it into Excel • Parse it into columns • Save as tab delimited text

  4. Codebook • Http://research.umbc.edu/~coates/ec611/owner_housing_codebook.html

  5. Stata • Read into Stata • insheet using “your file name” • Be sure you have 103 variables, 21720 observations

  6. Examine the Data • Sort control famtyp famrel • note multiple observations per control • what are famtyp and famrel? • Summarize data • several variables are strings • note minimums and maximums

  7. Housing Characteristics • Goal: estimate a hedonic equation for house characteristics • Consider Bedrms, Baths, halfb, kitch, dens, dining, famrm, laundry, living, rooms, unitsf, lot • What do you observe? • Look at maximums and minimums

  8. Housing characteristics • Functional form? • Does each bedroom contribute equally to value? • Does each bathroom? • What about area? • Price or log price? • Construction cost or purchase price?

  9. Regression • OLS - lprice as function of house characteristics, unitsf and lot • tabulate characteristics • tabu varname, gen(varname) • Sample weights [pw=pwt]

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