Linear Regression: House Pricing
Housing Prices in Blowing Rock, NC:
A Hedonic Analysis
Thomas Carter
Economics 4000
1. Introduction A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area. How are these characteristics used in determining the price? A model that is commonly used in real estate appraisal is the hedonic regression. This method is specific to breaking down items that are not homogenous commodities, to estimate value of its characteristics and ultimately determine a price based on the consumers’ willingness to pay. The approach in estimating the values is done by measuring the differences in the price of certain goods with regards to specific location. E.g. average cost of real estate is much lower in Missouri than in California. Location may be the biggest factor in real estate pricing.
2. Data and Regression Analysis My data is for Blowing Rock, NC. It’s a resort town in the Blue Ridge Mountains. The attractions here are mostly outdoor activities taking place in the secluded wilderness. The population is only about 1500 and the average cost of a house from my data is $485,839.50. For my linear regression, I am modeling the relationship between the price of homes, being my dependent variable, and some characteristics of the homes, being my explanatory variables. Originally my data consisted of the following for real estate in Blowing Rock, NC: price - selling price, miles from central business district, number of bedrooms, number of full bathrooms,
A Hedonic Analysis
Thomas Carter
Economics 4000
1. Introduction A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area. How are these characteristics used in determining the price? A model that is commonly used in real estate appraisal is the hedonic regression. This method is specific to breaking down items that are not homogenous commodities, to estimate value of its characteristics and ultimately determine a price based on the consumers’ willingness to pay. The approach in estimating the values is done by measuring the differences in the price of certain goods with regards to specific location. E.g. average cost of real estate is much lower in Missouri than in California. Location may be the biggest factor in real estate pricing.
2. Data and Regression Analysis My data is for Blowing Rock, NC. It’s a resort town in the Blue Ridge Mountains. The attractions here are mostly outdoor activities taking place in the secluded wilderness. The population is only about 1500 and the average cost of a house from my data is $485,839.50. For my linear regression, I am modeling the relationship between the price of homes, being my dependent variable, and some characteristics of the homes, being my explanatory variables. Originally my data consisted of the following for real estate in Blowing Rock, NC: price - selling price, miles from central business district, number of bedrooms, number of full bathrooms,
References: Lancaster , Kevin J., “A New Approach to Consumer Theory,” The Journal of Political Economy, Vol. 74, No. 2 (Apr., 1966), pp. 132-157.