US Real Estate Market: Booming Despite Recession Fears - Essay Sample

Introduction

The real estate market is defined as the market for properties such as land and buildings. The total value of the US real estate market was estimated at $33.3 trillion in 2018 (Lloyd, 2019). The market accounts for about 15% of US total GDP. The US housing market has enjoyed growth since the 2008 financial crisis. According to Richardson (2019), the market has continued its rapid growth despite fears of US recession. The market has seen an increase in home equity prices and house flipping (Richardson, 2019). The growth has been fuelled by strong economic growth, rising employment rates, and lower mortgage rates, among other favorable economic conditions. However, recent data indicates that the market is cooling off. The S&P/Case-Shiller home price index increased by 5.16% in the year to November 2018, a fall from the 6.09% growth in the previous year (Delmendo, 2019).

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The global real estate market was estimated at $228 trillion $280.6 trillion by the end of 2017 (Savills, 2019). According to the Global Property Guide (2019), the global real estate market continues to grow, despite the slowdown in some markets. House prices rose in 24 out of 45 countries in 2018, with strong growth in Asia and Europe (Global Property Guide, 2019).

Classification of the Real Estate Market

The real estate market is classified into commercial and residential markets.

Commercial real estate consists of property used for generating income. It includes retail property, hotels, office buildings, and industrial property. The US commercial real estate total market value was valued at between $14 and $17 trillion in 2018 (Thompson, 2019). In 2018, the US commercial real estate market generated total revenue of $1.1 trillion (Ibisworld.com, 2019).

Residential real estate market includes homes, condominiums, townhouses, and duplexes, among other property used for residential purposes. It constitutes the largest percentage of the real estate market worldwide. In the US, the residential estate market is estimated at $30 trillion, about 80% of the total market value (White, 2019).

Real Estate Market Tier Classification

The real estate market is classified into three tiers based on the stage of development of the market.

Tier-I cities have well developed and established real estate markets, consisting of desirable residential property, businesses, schools, and other facilities. The real estate market in Tier I cities are characterized by low growth rates and high prices. In the US, Tier I cities include New York, Boston, Washington D.C, San Francisco, and Los Angeles.

Tier-II cities have developing real estate markets. Such markets are associated with high growth rates but are yet to realize their full potential. Prices are lower in such as markets than in Tier I cities. In the US, Tier cities include Baltimore, Pittsburgh, Seattle, and Austin, among other cities. According to Zillow (2019), the median home price in New York City is $671,700, while that of Baltimore City is only $115,300.

Tier-III cities are characterized with poorly developed real estate markets. Prices are low and markets have strong growth potential (investment opportunities). Cities such as Centreville, Fort Scott, Wewoka, and Hamtramck may be classified as Tier III cities. For instance, the median house price in Wewoka is only $26,400 (Zillow, 2019).

How to Find Out the Appropriate Price

One can apply data scraping/crawling to find out the appropriate price for a house. Data scrapping involves importing data from a web source into a spreadsheet. This process enables one to analyze a wide range of data. A web search of house prices is conducted to identify sites with data on house prices. On Microsoft excel, the data can be imported from the web by copy-pasting the URL of the data source.

As shown in the Excel file (sheet 1), house price data was imported from Zillow.com. The data contains monthly data of median home values (2 bedroom houses) by neighborhood. The search can be customized to a period of interest. In this case, the data is restricted to 2019. The data can then be filtered to reflect areas of interest. Assuming a buyer is interested in determining house prices in Cleveland-Elyria, Cuyahoga County, houses in the region are filtered using Excel data table as shown in the table below.

The average median price for each size rank can be used as the appropriate price as shown in the table above. Other filters such as the number of bedrooms, size of the house, among other features, to determine the appropriate price.

NAHB or Other House Price Index

House price indices can be used to assess the value of a home. They indicate the change in the price of houses from one period to another. Sheet 2 of the excel file indicates US quarterly house price index (all transactions) with 1980 Q1 as the base. The price index for the first quarter of 2019 is 443.51, indicating that the current price is 4.43 times the price as of 1980. Therefore, if the value of a house in 1980 Q1 is known, the current price can be estimated using the price index.

For instance, if a house was valued at $120,500 in 1980 Q, the current price can be estimated as follows:

Current price (2019 Q1) = Price in 1980 Price Index 1980Q1

= 120,500 4.4351

= $534,429.55

Linear Regression

Linear regression is a model that expresses a dependent variable as a function of one or more independent (predictor) variables (Brooks & Tsolacos, 2014). Given the regression equation and predictor variables, the value of the dependent variable can be estimated. In this case, the price of a house can be expressed as a function of its features such as the size of the sitting room, the number of bedrooms, size of the lot, and year built, among other variables.

Using house sales data in Manhattan, a linear regression model was developed as shown below.

Price = 6125168.501 - 61951.8444bedrooms + 63903.61624bathrooms + 294.3087sqrt_living -3114.0375yr_built

The R-Squared of the model is 0.5568 indicating that the number of bedrooms, bathrooms, size of the living room, and the year the house is built, explain 55.6% of the variations in house prices (Brooks & Tsolacos, 2014). The F statistic of the model is 312.49, with a p-value of 0.000. This implies that the model is statistically significant and can be used to estimate house prices.

The p-values of each of the coefficient of the model are less than 5%. It implies that each of the coefficients is statistically significant. Thus, the number of bathrooms, bedrooms, size of the living room, and the year the house is built are significant influencers of the price of a home. It implies that the above factors should be considered when estimating the price of a house.

Illustration:

If a house built in 2010 has 3 bedrooms, 2 bathrooms, and the size of its living room is 1500 square feet, its price is estimated as follows:

Price = 6125168.501 - (61951.8444 3) + (63903.61624 2) + (294.3087 1500) - (3114.0375 2010)

= $249,367.70

References

Brooks, C., & Tsolacos, S. (2014). Real estate modelling and forecasting. Cambridge: Cambridge University Press.Delmendo, L. (2019). U.S. housing market gradually cooling. Retrieved 12 September 2019, from https://www.globalpropertyguide.com/North-America/United-States/Price-History

Global Property Guide. (2019). Global housing markets poised to slow, but strong house price rises continue in Europe and parts of Asia. Retrieved 12 September 2019, from https://www.globalpropertyguide.com/investment-analysis/Global-housing-markets-poised-to-slow-but-strong-house-price-rises-continue-in-Europe-and-parts-of-Asia

Ibisworld.com. (2019). IBISWorld - Industry Market Research, Reports, and Statistics. Retrieved 12 September 2019, from https://www.ibisworld.com/industry-statistics/market-size/commercial-real-estate-united-states

Lloyd, A. (2019). U.S. housing market value climbs to $33.3 trillion in 2018. Retrieved 12 September 2019, from https://www.housingwire.com/articles/47847-us-housing-market-value-climbs-to-333-trillion-in-2018

Richardson, B. (2019). Robust U.S. Housing Market Continues To Expand Amid Recession Jitters. Retrieved 12 September 2019, from https://www.forbes.com/sites/brendarichardson/2019/07/18/robust-us-housing-market-continues-to-expand-amid-recession-jitters/#75dfa1123298

Savills. (2019). 8 things to know about global real estate value | Savills Impacts. Retrieved 12 September 2019, from https://www.savills.com/impacts/market-trends/8-things-you-need-to-know-about-the-value-of-global-real-estate.html

Thompson, A. (2019). Total Size of U.S. Commercial Real Estate Estimated Between $14 and $17 Trillion. Retrieved 12 September 2019, from https://www.reit.com/news/blog/market-commentary/total-size-us-commercial-real-estate-estimated-between-14-and-17

White, S. (2019). 2019 value of U.S. residential real estate market near $30 trillion. Retrieved 12 September 2019, from https://finance.yahoo.com/news/2019-value-u-residential-real-141443420.html

Zillow. (2019). New York NY Home Prices & Home Values | Zillow. Retrieved 12 September 2019, from https://www.zillow.com/new-york-ny/home-values/