Investments II Assignment #2 (1)

docx

School

Tarleton State University *

*We aren’t endorsed by this school

Course

4301

Subject

Economics

Date

May 24, 2024

Type

docx

Pages

Uploaded by nancyeufracio101

1 Define Methodology and Sample Nancy Flores & Jordan Carson March 5, 2024 Dr. Esqueda Investments II Assignment #2

2 Introduction Housing prices in the Unites States have increased steadily over the past century, with the exception of the housing market crash in 2007. Leaving out the housing market crash, prices of homes have significantly increased as expected due to the growth of the American economy as a whole. However, there are potentially many factors that affect housing prices that are important and useful to examine. This paper analyzes the influence that mortgage rates, as well as the inflation rate shocks, have on housing prices. However, the main focus is on the relationship between prices and rates. It is helpful to understand the relationship between mortgage rates and housing prices for both lenders and borrowers as well as the recent history of the United States housing market. To illustrate how the variables can affect each other, it is useful to examine the housing bubble crisis that occurred in the Unites States in the early 2000’s. In the 2000’s is when the housing boom began and continued until about 2007-2008. Homebuyers were purchasing homes fearlessly. Eventually, prices stopped going up and peoples willingness to buy higher prices homes went down. We have come to the conclusion that mortgage rates are an effective indicator of the movement of housing prices, lenders and borrowers and the Federal Reserve. In this paper we will help examine the long-run and short- run effects by using a time series data. Methodology For our Methodology we decided to choose a time series model. A time series analysis is specifically designed for forecasting, these algorithms examine patterns in time-

3 based data, like past mortgage rates over months or years to predict future trends. In order to develop appropriate time series models to characterize housing price dynamics and develop an accurate model to forecast housing prices in the short term, we investigate linear times series models of the U.S. home mortgage rates and inflations rate shocks. First we start by formulating a time series model to show the variables in this study that expect to affect housing prices. As it was noted in the study by McGibany and Nourzad (2004) there is a very large amount of time-series data available for the variables incorporated in this study. However, in this study we have solely focused on data for United States. The time series model representing the long-run relationship between the dependent variable, housing prices and a few independent variables used are mortgage rate and gasoline price, crude oil, lumber prices, GDP growth, and inflation on the cost of materials to build a home. Below shown below is our formula: Dependent variable: HP = housing price Independent variable: ER = effective mortgage rate, GP = gasoline price CO= crude oil LP= lumber prices G= GDP growth ICOM= inflation cost of materials T= time HP t = β 0 + β 1 ER t , + βa 2 GP t + βa 3 CO t + βa 4 LP t + βa 5 GDP t + βa 6 ICOM t Variables There are many variables at play in this situation, we have our dependent variable which is home prices. We are conducting this study to not only determine the relationship between mortgage rates and home prices but also to see if other variables are part of the equation that

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

4 drives home prices. Our variable of interest is mortgage rates of course but some other controlled variables to note are crude oil and gas prices, lumber prices, GDP growth, and inflation on the cost of materials to build a home. Inflation affects many parts of the home-building process that strain is passed on from the homebuilder to the home buyer in the form of increased home prices. One study confirms that the average cost for materials to build a single-family home increased 42% from 2018-2021, which in turn caused the median sales price of new homes to rise by 20% from 2020-2021. This data is just the start of how home prices may be determined and shows that inflation and market decisions can drive home prices up or down. Looking at lumber prices and demand can be a great measure of what home prices may look like over time as well. In the second half of 2020, the demand for lumber used in the development of new homes, repairs, and remodeling jobs far exceeded the supply of lumber being produced by mills in the United States causing lumber prices to shoot up. By applying simple logic, we can see that home prices also jumped by about 12% from 2020-2021. These effects on the home market seem more drastic because 2020 was a time of economic strife with high inflation however, it is important to note that the lumber demand overtook the lumber supply which led to lumber prices going up. On the other hand, oil prices do not directly correlate with home prices like lumber prices do the underlying relationship is much more subtle. Oil prices can have a direct relationship with mortgage rates and because of that correlation we also correlate raised oil prices with higher mortgage rates which typically means higher home prices. On the other side of that coin, we have GDP growth which shows a positive correlation with housing markets and home prices. When GDP goes up trends show that average home prices also increase during that time.

5 Sample We decided to use a large sample size for two different reasons. We wanted to have the ability to gain accurate information over time while also highlighting trends that might occur such as times of GDP growth, economic prosperity, and inflation. Data over the past 50 years shows that the relationship between mortgage rates and home prices is more subtle in the short run and clearer in the long run. However, short-run or long-run inflation makes both mortgage rates and home prices rise. Oil prices over the last 50 years have more correlation with mortgage rates than they do with home prices. When the price of crude oil goes up and it is due to inflation and not foreign relations or conflict then mortgage rates also rise because of inflation which can drive home prices down in the short term. Lastly, when inflation affects the costs of materials used in building homes of course home prices rise leading us to establish this as a determinant of home prices in the short run and long run as well. Over the past 50 years home building materials such as building materials, bricks, and cement have been sensitive to inflation. Once those prices are driven up home builders must recoup the excess cost incurred to build new construction homes by increasing the price of those homes. Those were some inflation-related causes of mortgage rate and home price increases. We must look at a supply and demand cause that can increase home prices. Lumber can be affected by inflation but here as of recently, there has been a supply shortage due to the increased demand for lumber. This increased demand and lack of supply causes lumber mills to increase prices which leads to more expensive home construction jobs, home builders in turn charge more for the home to make a profit.

6 Works Cited Almutairi, H., El-Sakka, & MIT. (2016). Determinants of housing prices in an oil based economy. Asian Economic and Financial Review, 6(5), 247-260. doi:https://doi.org/10.18488/journal.aefr/2016.6.5/102.5.247.260 By, J. C. (2003, Jun 19). The economy: Prices of lumber rise, signaling A turnaround. Wall Street Journal Retrieved from https://acu.idm.oclc.org/login?url=https://www.proquest.com/newspapers/economy-prices- lumberrise-signaling-turnaround/docview/398816931/se-2 Müller Ralf; Klein, Gary. Project Management Journal; Newtown Square Vol. 51, Iss. 6, (Dec 2020): 579-581. Do lower mortgage rates mean higher housing prices? James M. McGibany, Farrokh Nourzad Pages 305-313 | Published online: 05 Oct 2004 “Time Series Modeling with ARMA to Predict Future House Price” Bonnie Ma Feb 2020

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Related Documents

Incoterms quiz.docx

Essay Assignment ECO 502 SQ 2024.docx

quiz3.pdf

ECO HW 5.doc

Week5.pdf

ECON 101 Midterm #1 Study Package.pdf

FINC 3302 - Assignment 2.docx

Quiz_1_BUS825.docx

Self-Quiz Unit 8.docx

page 4.pdf

Homework #8 Spring 2024.pdf

Research Project 1 Terry Smith (3).docx

Recommended textbooks for you

ECON MICRO

Economics

ISBN:9781337000536

Author:William A. McEachern

Publisher:Cengage Learning

Principles of Economics 2e

Economics

ISBN:9781947172364

Author:Steven A. Greenlaw; David Shapiro

Publisher:OpenStax

Essentials of Economics (MindTap Course List)

Economics

ISBN:9781337091992

Author:N. Gregory Mankiw

Publisher:Cengage Learning

Microeconomics A Contemporary Intro

Economics

ISBN:9781285635101

Author:MCEACHERN

Publisher:Cengage

ECON MACRO

Economics

ISBN:9781337000529

Author:William A. McEachern

Publisher:Cengage Learning

Principles of Microeconomics

Economics

ISBN:9781305156050

Author:N. Gregory Mankiw

Publisher:Cengage Learning

SEE MORE TEXTBOOKS

Recommended textbooks for you

ECON MICRO
Economics
ISBN:9781337000536
Author:William A. McEachern
Publisher:Cengage Learning
Principles of Economics 2e
Economics
ISBN:9781947172364
Author:Steven A. Greenlaw; David Shapiro
Publisher:OpenStax
Essentials of Economics (MindTap Course List)
Economics
ISBN:9781337091992
Author:N. Gregory Mankiw
Publisher:Cengage Learning
Microeconomics A Contemporary Intro
Economics
ISBN:9781285635101
Author:MCEACHERN
Publisher:Cengage
ECON MACRO
Economics
ISBN:9781337000529
Author:William A. McEachern
Publisher:Cengage Learning
Principles of Microeconomics
Economics
ISBN:9781305156050
Author:N. Gregory Mankiw
Publisher:Cengage Learning