Determinants of Residential House Prices in the US

Background

The real estate industry faces short and long-term challenges ranging from housing variability, availability, affordability, financing and ownership elements and their vast resounding impact on the economy’s stability. There is a big gap in pricing as properties differ largely in value across locations; this elasticity causes a rise in demand which automatically forces the prices up. Over the past decades, studies have demonstrated that the elasticities in house supply vary greatly from time to time and place to place (Aastveit et al., 2020). However, studies have also demonstrated that elasticities have reduced significantly after the coronavirus pandemic. The decline is seen in regions where stricter land laws and places where a high price drop was experienced as the cause of the pandemic.

Many investors in the housing sector look beyond the different attributes of prospective homes. Instead, they opt to concentrate on the potential a property has in value. The available literature on real estate elucidates a broad number of attributes to explain house prices. The cost of most if not all items needed to build a house in the US is ever on the rise; in some instances, the price has been doubled since the onset of the pandemic (Aastveit et al., 2020). But the issue of real state and provision of affordably low-cost houses have been a matter of concern for different sets of federal administrations that ruled in the past. The housing sector is marred by many challenges that spread from low-level neighbourhoods to posh estates – these challenges present to all aspiring homeowners and rental residents (Von Hoffman, 2012). Nevertheless, the demand for housing is ever-present, and the supply is not sufficient for the suppliers to fulfil. Therefore, the journey to homeownership is quite challenging, painting housing as an extreme symptom of modernization reality and its pressure from the booming economy.

Literature review

The real estate cost in the US had increased by approximately 30% since 2012 when housing recovery commenced – this is quite similar to the trends witnessed during the Housing boom between 1996 and 2006 (Aastveit et al., 2020). Nevertheless, despite the occurrences in house prices, the supply has not even come close to meeting the demand – considering the housing expansion policy in effect. The 1950s marked the beginning of a new era where many people relocated from the rural areas and preferred settling in a town like setting (Fazal et al., 2015). Expansion of urban areas and cities prompted an upsurge of commodities such as houses and land, which were in limited supply. Few house units and the absence of a resound real estate industry contributed largely to the slow response to the looming shortage (Fazal et al., 2015). The gaps in real estate prompted businesses and many people to search for cheaper alternatives to fulfil their needs.

After the second world war, many people moved into urban places, making them overcrowded. As a result, the urban centres spilt over to the suburbs and consequently to the cleaner rings of land outside the towns, which became preferred places for occupation. The expansion train and trend continues to define how towns and cities grow to this day. With this understanding, the value of land and houses in the suburbs increased drastically, and the status of some regions changed to prime. The rapid rise of modern cities and towns attracted massive efforts in construction which occurred within and around the urban areas. Evidently, there was a visible shortage in housing after WWII, a situation that prompted the federal administration to pass The Housing Act 1949 (Von Hoffman, 2012). The policy’s objective was to champion efforts to provide decent homes that provided a suitable living environment for all citizens and their families.

Despite the visionary goals, there were various challenges to solve; for instance, there was little development locally because most resources had been diverted to sustain the war. Also, many residential units were established around industries. The lack of houses was further pressured by the influx of war veterans who returned home as civilians (Fazal et a., 2015). Luckily government action to offer loans to the returning soldiers enabled them to purchase a house. Additionally, many families used their savings towards acquiring homes for their stay (Aastveit et al., 2020).

The presence of the Levitt brothers in the construction industry boosted the campaign to provide habitable spaces for many people; however, it was obvious that the real estate sector was not able to effectively react to the incredible housing demand in the US (Aastveit et al., 2020). After the war, the construction industry was highly fragmented and disorganized; the lack of an upsurge in building efforts exposed Americans to steeply rising prices of apartments, homes and other properties.

Data

The study will utilize secondary data collected from high-quality databases, including the US Bureau of Labour Statistics and FRED economic data. The data for the variables included in the study will be collected in the following manner;

Data for house price index in the US, Homeownership rate and the number of new houses built will be collected from FRED economic data available at https://fred.stlouisfed.org/categories/97. Data for GDP, Inflation rates, and the unemployment rate will be collected from the US Bureau of Labour Statistics available at; https://www.bls.gov/eag/eag.us.htm. These two databases are credible and contain high-quality economic data that will be used to test the study’s hypotheses. The databases are freely available to the public. Therefore, they would require no source contact or permissions.

Upon collection, the data will be cleaned by arranging the variables based on 2000-2020. The cleaning will also involve ensuring that all data points in the dataset have values. This will increase the quality of the data and the subsequent analysis.

Econometric analysis

The Ordinary Least Square regression model (OLS) is the preferred econometric analysis that will be used to examine the research question of interest in this research study is the Ordinary Least Square regression model (OLS). According to Sheffet (2017), the OLS is ideal in examining the relationship between a set of independent factors and a dependent variable. The OLS is ideal for this study because relationships between a single dependent variable and several independent factors will be examined. The potential issue when using OLS is based on assumptions that must be met before the analysis can be conducted. For this study, appropriate data will be collected, cleaned, and analyzed correctly to ensure all regression analysis assumptions are met.

Since the OLS regression will be used for making the analyses and testing the hypotheses, the following equation is expected to guide the analytical process;

Y = β₀X₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + E,

Where Y = house prices, X₁ = home ownership rate, X₂= Number of new houses built, X₃ = GDP, X₄ = inflation rates, X₅ = unemployment rates and E = error.

This equation will be used to predict the housing prices from the selected independent factors.

Expected results

After appropriate data analysis, a significant relationship is expected between the house price index and the independent factors. The inflation rate, unemployment, number of new houses built, GDP, and homeownership rate are expected to predict house prices. The study expected to conclude that these factors directly result in the decrease in house prices witnessed over the past two decades.

The original contribution of this research study is that it will document and provide possible economic answers to the problem of low residential house prices in the US. Therefore, the study will help business persons and investors in the real estate business make informed decisions when choosing to invest in this market. For policymakers, the recommendations from this study will improve policy formulation approaches, especially in relation to policies that affect the economy of the country and the housing sector in particular. Recommendations from this study will focus on ways to address the causes of the low housing prices. The conclusions from this study will pave the way for discourse around a potentially harmful issue and concern for business persons across the country.

References

Fazal, S., Banu, N., & Sultana, S. (2015). Expanding cities, contested land: role of actors in the context of peri-urban interface. Current Urban Studies, 3(03), 187. https://doi.org/10.4236/cus.2015.33016

Von Hoffman, A. (2012). History lessons for today’s housing policy: The politics of low-income housing. Housing Policy Debate, 22(3), 321-376. https://doi.org/10.1080/10511482.2012.680478

Sheffet, O. (2017, July). Differentially private ordinary least squares. In International Conference on Machine Learning (pp. 3105-3114). PMLR.

Aastveit, K., Albuquerque, B., & Anundsen, A. K. (2020, February 25). The declining elasticity of US housing supply. VOX, CEPR Policy Portal. https://voxeu.org/article/declining-elasticity-us-housing-supply