Linear Regression Model for Predicting Housing Prices in the United States

University
College Algebra
By
Your Name
Date
Page 1 of 11
© <Your Name> 2019

College Statistics
LR-1
Purpose
The price of housing in advanced economies has had a constant substantial
growth pattern over the recent years. However, experience show that the
prices of the houses could disunite from the existing equilibrium and
sustainable levels due to the adjustments that affect macroeconomics and
financial stability, creating a need to constantly monitor houses prices and
assess whether they will be sustainable (Geng, 2018). The purpose of the
project is to develop a linear regression model that can be used to predict
the average annual sales prices for new homes sold in the United States with
an aim to assess whether the prices will be sustainable.
Data
The housing data was collected from
https://www.census.gov/construction/nrs/index.htm. The housing prices were
gathered for the past seventeen years (2000-2016) but more data going as a
far behind as 1963 is available.
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Average Sales Prices of New
Homes Sold in United States
Period Average Annual Cost
2000 $207,000
2001 $213,200
2002 $228,700
2003 $246,300
2004 $274,500
2005 $297,000
2006 $305,900
2007 $313,600
2008 $292,600
2009 $270,900
2010 $272,900
2011 $267,900
2012 $292,200
2013 $324,500
2014 $347,700
2015 $352,700
2016 $360,900
LR-2: SCATTER PLOT
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The average annual cost follows an upward trend through the years. This is
the expected case because as the years pass the population increases
thereby increasing the value of land and hence the cost of housing.
LR-3: Line of Best Fit (Regression Line)
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© <Your Name> 2019