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34 Housing Analysis

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Colorado State University Global Campus
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Housing Price Forecasting
Carlos Figueroa
Colorado State University Global
MIS470: Data Science Foundation
Kelly Wibbenmeyer
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Housing Price Forecasting
Introduction.
The importance of Housing Price Forecasting is undeniable, as Real State is one of the
most critical sectors in the economy. The 2009 subprime financial crisis revealed that as an
asset class, Real State is interconnected to the rest of the economic system through the financial
system due to leverage, collaterals, and the securitization of loans. Therefore, forecasting
models should be widely studied.
Housing prices impact the formation and burst of bubbles, macroeconomic processes,
such as business cycles, unemployment, aggregate consumption, etc. While other assets such
as bonds, commodities, and currencies have different price dynamics, Real State has specific
characteristics because of its heterogeneity due to property location and physical attributes.
(Ghysels, Plazzi , Tourus, & Valkanov, 2013)
Housing price predictability faces many challenges. For a start as an asset class,
Housing Prices are infrequently traded. Therefore, Real State data is relatively short, unlike
other assets such as bonds, commodities, and currencies that generate yearly, monthly, daily
data, and even by-minute data. Also, house prices face high transaction costs, and they are
inherently illiquid. (Ghysels, Plazzi , Tourus, & Valkanov, 2013)
Acknowledging the possible limitations, we wanted to have a first approach to analyze
and forecast Housing Prices based on the sale prices in Ames, Iowa. We wanted to know which
factors are significant to our model and how good is our prediction. We use a data set of 1,000
observations to fit a linear regression model, and then with a different data set, we tested how
well the predicted prices fit the observed prices.

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1 Housing Price Forecasting Carlos Figueroa Colorado State University Global MIS470: Data Science Foundation Kelly Wibbenmeyer Due date 2 Housing Price Forecasting Introduction. The importance of Housing Price Forecasting is undeniable, as Real State is one of the most critical sectors in the economy. The 2009 subprime financial crisis revealed that as an asset class, Real State is interconnected to the rest of the economic system through the financial system due to leverage, collaterals, and the securitization of loans. Therefore, forecasting models should be widely studied. Housing prices impact the formation and burst of bubbles, macroeconomic processes, such as business cycles, unemployment, aggregate consumption, etc. While other assets such as bonds, commodities, and currencies have different price dynamics, Real State has specific characteristics because of its heterogeneity due to property location and physical attributes. (Ghysels, Plazzi , Tourus, & Valkanov, 2013) Housing price predictability faces many challenges. For a start as an asset class, Housing Prices are infrequently traded. Therefore, Real State data is relatively short, unlike other assets such as bonds, commodities, and currencies that generate yearly, monthly, daily data, and even by-minute data. Also, house prices face high transaction costs, and they are inherently illiquid. (Ghysels, Plazzi , Tourus, & Valkanov, 2013) Acknowledging the possible limitations, we wanted to have a first approach t ...
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