Why is America’s center of gravity shifting South and West?

Ever since Florida surpassed New York as the third most populous state in the nation, journalists started to document the ways in which the South region of the United States began attracting young sun-lovers enthusiasts. Two factors have been identified as drivers of an apparent migration from the north towards the south. On one hand, real estate prices have been arguably one of the major causes for people heading south. On the other, employment growth and better job opportunities allegedly support decisions on moving out regionally. This article checks empirical data on those two factors to determine the effect on population growth of major cities in the United States. The conclusion, in spite of the statistical model limits, indicates that employment dynamic seems to drive a slightly higher level of influence in population growth when compared to housing costs.

Is it because of real estate prices?

The first factor some prominent people have identified is real estate prices. Professor Paul Krugman highlighted in his NYTimes commentary of August 24th, 2014 that the most probable reason for people heading south is housing costs, even over employment opportunities. From his perspective, employment has little effect on such a change given that wages and salaries are substantially lower in southern states when compared to the north. Whereas, housing costs are significantly lower in southern regions of the country. Professor Krugman asserts that “America’s center of gravity is shifting South and West.” He furthers his argument “by suggesting that the places Americans are leaving actually have higher productivity and more job opportunities than the places they’re going”.

By Catherine De Las Salas

By Catherine De Las Salas

Is it because of employment opportunities?

Otherwise, Patricia Cohen –also from the NYtimes- stresses the relevance of employment opportunities in cities like Denver in Colorado. In her article, the journalist unfolds the story of promising entrepreneurs immersed in an economically fertile environment. The opposite situation to that prosperous environment happens to locate northeast of the United States. Cohen writes that not only “in the Mountain West — but also in places as varied as Seattle and Portland, Ore., in the Northwest, and Atlanta and Orlando, Fla., in the Southeast — employers are hiring at a steady clip, housing prices are up, and consumers are spending more freely”. Her article focuses on contrasting the development of urban-like amenities and how those attractions lure entrepreneurs.

A brief statistical analysis of cross-sectional data.

At first glance, both factors seem to be contributing factors for having an effect on migration within states. However, although both articles are well documented, neither of those readings goes beyond anecdotal facts. So, confirming those very plausible anecdotes deserves a brief statistical analysis of cross-sectional data. For doing so, I took data on estimated population growth for the 71 major cities in the U.S. from 2010 to 2015 (U.S. Census Bureau), and regressed it on the average unemployment rate in 2015 (U.S. Bureau of Labor Statistics), median sale price of existing houses for the same year (National Association of Realtors), and the U.S. Census Bureau’s vacancy rate for the same year and cities (Despite that the latter regressor might be multicollinear with sale price of existing houses, its inclusion in the model aims at reinforcing a proxy for housing demand). The statistical level of significance for the regression is a 90 percent confidence interval.

Results.

The results show that, for these data sets and model, the unemployment rate has a bigger effect on population growth than vacancy rate and median home sale prices altogether. The regression yielded a significant coefficient of -2.78 change in population growth as unemployment decreases. In other words, the lower the unemployment rate, the greater the population growth. A brief revision of empirical evidence shows that, once the coefficients are standardized, unemployment rate causes a higher effect on the dependent variable. If we were to decide which of the two factors affects population growth greater, then we would have to conclude that employment opportunities do it largely.

Regression Results.

Regression Results.

By using these data sets and this model, the employment dynamic seems to drive a slightly higher level of influence in population growth, when compared to housing costs. The unemployment rate has a standardized effect of negative 56 percent. On the other hand, median sale price of houses pushes a standardized change effect of 23 percent. Likewise, vacancy rate causes in the model an estimated 24 percent change in population change. Standardized coefficients are a tool meant to allow for disentangling the combined effect of variables in a model. Thus, despite that the model explains only 35 percent of population growth, standardized coefficients give insights on both competing factors.

Limits of the analysis.

These estimates are not very reliable given that population growth variable mirrors a five years lapse while the other variables do so for one year. In technical words, the delta of the regressand is longer than the delta of the regressors. For this and many other reasons, it is hard to conclude that employment constitutes the primary motivation for people moving out south and west. Nonetheless, this regression sheds light onto a dichotomy that needs to be understood .

Eight Data Sources for Research on U.S. Housing Market.

The National Association of Realtors communicated today that its index of Pending Homes Sales increased 3.5 percent in February 2016. This indicator offers valuable insight for housing market analysis here in the United States. Indeed, the index makes up a leading indicator of housing market and forecasts since it is based on signed real estate contracts, including single family homes, condos and co-ops. The relevance of tracking this index’s evolution, and other metrics listed herein, stems from the fact that the Great Recession originated presumably from failures within the regulation of the housing market.

By Catherine De Las Salas

By Catherine De Las Salas

Although the Pending Homes Sales moved upwards on February, this news is contradicting the long term trend of Home Ownership rate, which has been steadily declining since the beginning of the Great Recession. This fact could be pointing to a fascinating development in the sector. Precisely, these type of contradictions is the reason the U.S. housing market has become so intriguing for researchers, especially since toxic Mortgage Backed Securities triggered the Great Recession in the United States.

There are several resources at hand for advancing research in U.S. Housing Market. The ones that econometricus.com monitors frequently are the following:

  1. Pending home Sales. Data Source: National Association of Realtors.
  2. Case-Shiller Home Prices Index. Data Source: S&P Down Jones Indices.
  3. House Price Index. Data source: U.S. Federal Housing Finance Agency.
  4. Existing Home Sales. Data Source: National Association of Realtors.
  5. New Residential Construction. Data Source: U.S. Census Bureau.
  6. Housing Market Index. Data Source: National Association of Home Builders.
  7. Housing Vacancies and Home Ownership. Data Source: U.S. Census Bureau.
  8. Construction Put in Place. Data Source: U.S. Census Bureau.

Moreover, some of the most trusted housing sector metrics were proposed after the Great Recession (2009). For those who consider that the Great Recession was not an exclusive event of banking leverage, complexity and liquidity (learn more on this issue here), the following measures may shed light on valuable research questions and answers. In other words, flaws in the supply side of the housing market –Mortgage lending banks- might have had an impact in spreading the Great Recession, but, more importantly, the demand side could have had a more relevant role in triggering the crisis. Thus, these data may help researchers in explaining when and why mortgages went underwater in the first place.

Finally, Econometricus.com helps clients in understanding the economic relationship between a specific research and the United States’ Housing Market environment. Applied-Analysis can be either “Snapshots” of the Housing Market in U.S. Economy or historical trends (Time-series Analysis). Clients may simplify or augment the scope of their research by including these important variables in their models.

Follow up on US Construction Industry Data.

Follow up on US Construction Industry Data.

At the beginning of the summer of 2015, both labor statistics on employment levels and US Gross Domestic Product showed a slowdown on job creation coming from construction related activities. Given that the summer represents a time window for developers to build fast thanks to good weather conditions, economists always expect summer job increases to largely stem from construction. However, it was not the case for the summer of 2015, which alerted analysts to look cautiously at construction investment. On the first week of July, Econometricus.com poked on construction investment by looking at statistics on Construction Put in Place (US Census Bureau) for the month of May of 2015, as a way to find out whether or not construction investments had slowed-down effectively. Data on such a metric revealed no statistically significant change, which accurately corresponded to data reflecting job creation from the US Bureau of Labor Statistics, and data on GDP growth. Now that the summer is almost gone, it is worth looking at Residential Construction to either dissipate or collect more concerns.
July’s Construction Data from the US Census Bureau and the US Housing Department.

On annual basis increases were significant, but on monthly basis they were not so much. For instance, projected economic activity on residential construction increased significantly in aggregate terms for Approved Building Permits, Housing Starts, and Housing Completion, for the month of July 2015. On one hand, and in spite of a decrease from the previous month of June, plans to build housing units jumped 7.5% when compared to the month of July 2014. Likewise, Housing Starts augmented by 10% in July 2015 when compared to the same month of 2014. In terms of Housing Completion, which shows how fast contractors wanted to finish their work during the summer, privately-owned completed units skyrocketed by 14.6% in July 2015 vis-à-vis July 2014.

Construction summer statistics by region.

Regionally speaking, so far this summer the South has shown decent pace of Housing Completion growth. But, it is not the same case everywhere else. In the West region, privately-owned Housing units completed has declined steadily since summer 2015 started. In the Midwest, although July represented a rebound for the statistic, the numbers dropped to winter season levels. Currently the rate of Completed units is a bit higher than it was a year before though. On the other hand, the Northeast region bounced back after a big drop in June 2015. The graph below shows the trajectory for New Privately-owned Housing units completed, in which the blue line represents the Northeast region. The region’s statistic is back at the level it was one year before.

Privately-Owned Housing Units.

Privately-Owned Housing Units.

Therefore, coming up with a set of conclusions, to determine whether or not housing is holding back economic growth and job creation, is really hard at this point of the year. Having seen what we have observed so far, it is tough to adventure hardcore statements. However, except by the South region, Construction has experienced a slow-down all over the United States during the summer of 2015, which is reflects on both indicators, jobs and GDP Growth.

United States Housing Units Completed on July 2015.

United States Housing Units Completed on July 2015.

 

Northeast Housing Units Completed on July 2015.

Northeast Housing Units Completed on July 2015.

 

Midwest Housing Units Completed on July 2015.

Midwest Housing Units Completed on July 2015.

 

West region Housing Units Completed on July 2015

West region Housing Units Completed on July 2015

 

South Region Housing Units Completed on July 2015.

South Region Housing Units Completed on July 2015.

 

 

In July’s retail sales, Food and Drinking Services is King.

With some exceptions, retail sales for the month of July showed positive signs. As the summer season fades down, July sales’ advance estimates show good increases in food and drinking services (9%), Furniture (6.1%) and Building materials (2.8). Those three lines boosted the annual change in sales, according to data from the U.S. Census Bureau. In aggregated terms, the most significant changes were in food services, which showed a 2.4% increase when compared to the same month 2014.

Food sales July 2015

Food sales July 2015

 

Retail trade sales were little changed from July 2014 given a sharp decline in Gasoline sales, electronic appliances and sales at Department Stores. Regarding gasoline, it is most than natural that the nominal value had deceased since oil price is still at record lows. On the other hand, Health and personal care store sales were 3.1 percent change along with the same figure for Clothing and accessories stores. Sales of Sporting goods, hobby, books and music increased by 6.4 percent when compared to the same month 2014.

Health sales July 2015

Health sales July 2015

Good news were mostly on sales of Furniture and Building materials. Sales of Furniture stores increased by 6.1 percent, while Building materials, garden equipment and supplies did so by 2.8 percent. Electronics, as already mentioned, declined on sales by -2.5 percent.

Furniture Sales July 2015

Furniture Sales July 2015

Retail sales in Department stores also declined by -2.7 percent. Other retailers such as miscellaneous stores and nonstores retailer increased their sales in July by 3.1 and 6 percent respectively.

Dept Stores Sales 2015

Dept. Stores Sales 2015

Preliminary Data on Services Industries during first quarter 2015.

The more data we know, the more facts we start to understand about the U.S. GDP contraction on the first quarter of 2015. The United States Census Bureau released on June 10th its preliminary data on the Services Sector Industries. Before adjusting by season and price changes, the data unveil unsurprisingly losses in Revenue for Utilities Industry, which comprises Gas and Electric distribution companies. The decrease for Gas related industrial activities was around 16% when compared to the same quarter in 2014. Likewise, Transportation would be hit with a decreases in revenue from Inland Water Transportation activities (3.4%); as well as Pipeline transportation activities (5%). The Utilities Industry as a whole would decrease its revenue in 6.1% in 2015Q1 compared to 2014Q1. Transportation and Warehousing increased revenue in 2.6%, though. An estimated of 551k employees work in the Utilities Industry.

The other losses in revenue would be seen in Newspaper publishing companies. Following the data, the activity continues to decline as Other Information Services activities soar. Newspaper publishing and related activities would drop its industry revenue by 3.8%. Otherwise, Other Information Services is said to have increased its revenue in 9.1% in the first quarter of 2015. Other Information Services comprises publishing activities like econometricus.com. Periodical publisher, Book Directory and Mailing list industries apparently declined by 6.1% and 2% respectively. Motion pictures and Sound Recording industry could have contracted its revenues by 2.4%, while Broadcasting (except internet) dropped by 0.6%. The activities on Specialized Design Services may have lost 6.6% in revenue during the first quarter of 2015.

The good news in Service Sector revenues will be apparently in the Education Services Industry, which will show, if confirmed, increases near 10.4% in 2015Q1. Administrative Support Businesses is also said to have had one of the best economic performance with an alleged increase in Revenue of roughly 6.8% when comparing 2015Q1 vis-à-vis 2014Q1. In the third place of revenue positive variation would be Real Estate which increased 5.7%, closely followed by Professional, Scientific, and Technical Services that did so by 5.6%. Accommodation Industry, also preliminary, showed an increase of 5.6%. Health Care percent change in revenue is estimated to be around 3.1.

 

“Survey Description”:

“The U.S. Census Bureau conducts the Quarterly Services Survey (QSS) to provide national estimates of quarterly revenue for employer firms located in the United States and classified in select service industries. The current total sample size is approximately 19,000 employer firms” (U.S. Census Bureau).

Did the housing market affect negatively economic growth in 2015Q1?

Recent news on GDP 2015Q1 have many economists wondering about the possible domestic causes for such a negative growth (-.7%). The U.S. Bureau of Economic Analysis (BEA) did not hesitate in pointing out towards Investment in non-residential structures, which decrease 20%. Perhaps, data on housing market from both Construction Spending and Existing Home Sales might advance clues on what is going on in the U.S. economy currently. First, preliminary data on Construction Put in Place might shed light into what BEA signaled earlier, and data on Existing Housing Sales may complement an explanation, at least for as far as to the domestic economic dynamic concerns.

A

First, the Total Value of Residential Construction Put in Place in the U.S. economy decreased by 1.8% when comparing April 2014 to the most recent estimated statistics from the U.S. Census Bureau for April 2015. The estimated value for Private Residential Construction in April 2015 was roughly 353,086 million dollars, which totals 7,740 million less put in place than in April 2014. In spite of the decrease during April, official at the U.S. Census Bureau stated that “during the first 4 months of this year, construction spending amounted to $288.7 Billion, 4.1 percent (+/-1.5) above $277.3 Billion for the same period 2014”.

B

Perhaps the deceleration for the sector is being brought by Residential and Power sectors. The preliminary value of construction put in place for Residential and Power -type of constructions- went down during April 2015 inasmuch of -6,417 and -11,657 million dollars correspondingly, much of which came from a decrease of roughly 7,850 million dollars less pertaining the private sector and -3,808 million dollars less from the public sector. Though, the overall account got offset by increases in Manufacturing, Transportation and Commercial.

C

Since most of Construction Spending indicators went up in April 2015p, the question to ask economists is to whether or not the housing market actually slowed down economic growth during the first quarter of 2015; at least for the domestic side of the U.S. economy. Construction growth in Lodging and Commercial industries went up both by 17%, while Offices and Recreation related constructions did so by roughly 20% (April 2014 compared to April 2015p).

D

Data Source: U.S. Census Bureau. Data Overview: “The Value of Construction Put in Place Survey (VIP) provides monthly estimates of the total dollar value of construction work done in the U.S. The United States Code, Title 13, authorizes this program. The survey covers construction work done each month on new structures or improvements to existing structures for private and public sectors. Data estimates include the cost of labor and materials, cost of architectural and engineering work, overhead costs, interest and taxes paid during construction, and contractor’s profits. Data collection and estimation activities begin on the first day after the reference month and continue for about three weeks. Reported data and estimates are for activity taking place during the previous calendar month. The survey has been conducted monthly since 1964”.

US Census Bureau’s Advance Estimates show Retail Sales increased 3.2% in December 2014.

Estimated Sales Dec 2014 - 2

Advance Estimates for Retail and Food Services show that Sales for December 2014 increased 3.2 percent compared to December 2013. Statistics were adjusted by season, but not corrected for inflation, which gives a total estimated sales volume of U$442.9 billion. Auto and other motor vehicles dealers were up 9.2 percent when compared to same month of 2013. Food and services also grew in sales roughly 8.2 percent. The Advance Estimates give a preliminary hint by sampling a smaller base of the full Retail Trade and Food services Survey at the US Census Bureau.

Estimated percenrt change
Almost all of the survey accounts experienced increases when compared to the same month in 2013. However, Gasoline Stations –as expected given low oil prices- realized 14.2 percent less dollars sales this past December 2014. Also, estimate data show that Department Stores sold 0.6 percent less. Food Services and Drinking places reported in advance the highest gains for the season with a strong 8.2% as well as Auto sales with a similar statistic. Health and Personal care stores also increased their sales level by 6.1 percent. Furniture and Electronics and Appliances sold 5.8 percent and 6.8 percent respectively, preliminary data showed.

Housing building fatigue: an alternative interpretation of the origin of the Great Recession.

Economists have pointed out three contributing factors for the Great Recession. All of them closely related to the financial and banking system: Leverage, complexity, and liquidity (Blanchard, 2013). In this article, we try to advance an alternative interpretation of the origin of the Great Recession by looking at the housing market and to its links to mortgage borrower’s expectations. More precisely, we look at one reason why a Mortgage-backed loan (MBL) may go underwater: that is, a “Mortgage default Risk derived from housing building fatigue”. Here, we advance the first hypothesis of a series of three that compose the entire proposal. We assume that the Great Recession was largely an effect of a substantial decrease in consumer confidence. Likewise, we consider that the Great Recession has arguably many similarities with the U.S. Recession of 2001. The work cites those two crisis in order to determine stylized facts of U.S. Recessions.

On the third part of the article we look at the logic behind the boom of the housing prices. We aim at situating the discussion in the frame of capital inflows to the housing market. We see this as a reasonable movement since nominal interest rates in the financial markets were at record lows. After presenting the macroeconomic context based on the stylized facts, we propose a model of what we call “Mortgage default Risk derived from housing building fatigue”, followed by some linear regression to estimate the correlation of the first hypothesis. Finally we present some conclusion about the limits and the open windows for further research.

Hypothesis:

We propose the following hypothesis as a contributing factor to the origin of the Great Recession: default risk in mortgages payments are higher for low income Americans and old housing buildings mortgages exacerbate the risk. There is a correlation between low income Americans dwelling in aged housing buildings. There are reasons to believe that a mortgage loan goes underwater more often as the housing building gets older. This basically means that the older the building, the greater the chances for default of payments in the mortgage.

We put forth the following three hypothesis extending the contributing factors of the Great Recession:
Hypothesis number one: low income Americans tend to live in aged housing buildings.
Hypothesis number two: mortgage loans go underwater more often as the housing buildings get older.
Hypothesis number three: default in mortgages payments are higher for both low income Americans as for old housing buildings mortgages loans.

Methodology:

We use descriptive statistics to give an account of our hypothesis. We first take data from the American Housing Survey which is processed by the United States Census Bureau. This data inquire into the various aspects of housing buildings. Data on age of buildings and household income are described used such a survey. Then, some simple linear regression are run for establishing the correlation of the first hypothesis. We certainly acknowledge limitations of the data base and the conclusions we are making. Nonetheless, the aim of this paper is to point out some aspects of the hypothesis so that it can be tested in either larger samples or by using panel comparative data. Not enough numbers of observation may be the main source of statistical violations methodologically speaking.

Stylized facts of the Great Recession:

Stylized facts of the Great Recession relate to the analytical framework of IS-LM model. More precisely, stylized facts refer to the immediate effects of the financial crisis over the macro economy. It is arguably accepted that, in terms of the IS-LM model, the IS curve shifted sharply to the left thereby fostering the Great Recession (Graph # 1). The Great Recession was largely an effect of both, a substantial decrease in consumer confidence preceded by a large increase in interest rates. The United States Government reacted wisely but late. By the time the U.S. government realized there was a crisis in the economy, the Great Recession had spread throughout the economy. By 2008 Banks were reluctant to lend to each other, which is a signal of how pervasive Mortgage Backed Securities were considered to be (Blanchard, 2013). After Lehman Brothers declared bankruptcy in September 15th 2008, the Government of the United States decided to intervene by putting in place two programs: the Troubled Asset Relief Program (TARP); and later on, the American Recovery and Reinvestment Act (ARRA). TARP was intended to back toxic assets already in the market, whereas ARRA meant an increase in government deficit (Blanchard, 2013). Clearly, both ARRA and TARP consisted of an effort to use fiscal policy as a tool for pushing back the IS curve, since it is hard to assume TARP as a monetary tool.

A more detailed account of the events that fostered the Great Recession focus at the role of banks in the economy. Economists have pointed out onto three contributing aspects across the financial system: Leverage, complexity, and liquidity (Blanchard, 2013). First, bank leverage went at high level during the Great Recession. Through the usage of “innovative” financial instruments, Banks were able to hide off-balance-sheet leverage ratios (Kalemi-Ozcun, S. et al, 2011). Second, complexity. The growth of securitization of Mortgages-Baked loans (MBS) and the proliferation of Collateralized Debt Obligations (CDO) helped lending risk to hide too. Rating agencies were unable to detect and discriminate toxic assets grouped in large security bundles (Horton, B. 2013). Finally, liquidity related regulation was circumvent by Banks via Credit Default Swaps CDS. Through the issuance of security insurance, Banks were able to mask their liabilities with respect of the level of risk they took. In other words, riskier mortgages loans or Subprime loans were masked riskless throughout CDSs and bundled into MBSs (Blanchard, 2013). This structure helped the crisis to spread out deeper the financial system. These three aspects cogently support the Great Recession explanations in regards to the financial system.

Generally speaking, a fall in consumer confidence affects consumption negatively. Given that investment depends partially on sales, a fall in consumer spending will make non-residential investment to drop. Private savings will decrease since consumption depends on income. The overall effect of a decrease in consumer confidence would eventually lead to a deferral of household spending whereby hurting output through a deferral of non-residential investment.

Graph 1.
1
Thus, housing prices increased from 2,000 to 2,006, which in some indexes such as the Case-Shiller that increase represents more than 200% change in less than a decade (Blanchard, 2103). This increase in demand for loans was outcome of low interest rates in mortgages. Economists have come into a consensus that during 2,000 and 2,006 Banks apparently were more willing to lend to borrowers, to the extent that by 2,006 20% of the U.S. loans were mortgages (Blanchard, 2013). Additionally, both borrowers and lender seemed to agree on the assumption that housing buildings do not depreciate over time.

U.S. Recession of 2001:

To some extent the Great Recession has many similarities with the U.S. Recession of 2001. The years preceding 2001 were characterized by a similar economic expansion. Though investment declined 4.5% in 2001 (Blanchard, 2013). Data at the time did not show negative growth nor signals of slowdown. Both Recession were triggered by a decrease in autonomous spending. The Fed pursued a very expansionary monetary policy after realizing there was a recession in the U.S. economy. The Fed chair of the time, Alan Greenspan, called the preceding expansion that led the economy to the slow down, a period of “irrational exuberance” (Blanchard, 2013). There were high levels of economic optimism that lasted for almost a decade before 2001. On the fiscal policy side, President George W. Bush pushed through legislation that lowered income taxes and increased spending in homeland security sector. On the monetary policy side, the Fed increased the money supply in 2001. Graph #2 depicts the effect of a decline in investment demand as well as the effects of the policies put forth by the government. Note that both graphs look similar.

Graph # 2.

2

Nonetheless, as Horton (2013) point out financial derivatives have always existed and performed well. Yet an explanation that considers only the financial crisis seems to be not sufficient. Perhaps, a deeper look into consumer expectations and into the history of the housing market may shed light onto some complementing conclusions. Thus, the model presented in this paper advances an emulation of the real interest rate calculation as the interest rate in terms of the housing prices. In the context of low nominal interest rates, analysis considering either the appreciation or depreciation of houses prices become more relevant. Investor’s decision on whether to hold bonds or equity on a house makes the difference for the real estate market, and particularly to the housing market. When interest rates are at low levels it is expected that investments seeking profits turn into the real sector. Even more, under the perceived reality that housing prices do not depreciate with time.

Capital inflows to the housing market are reasonable movements since nominal interest rates are at records lows. Additionally, expected inflation has been also around 1.1% (Blanchard, 2013). Both nominal interest rate and real interest rate in the American economy have been moving closely since 1995, with both fluctuating below 4% (Blanchard, 2013). This is an outcome of the so-called liquidity trap. Monetary institutions in the United States aim at stimulation economic growth by lowering nominal interest rates. The outcome has been no so much of an increase of household spending but a mobility of capital into more profitable markets.

The effect of changes in interest rate comes in two places. First the effect nominal interest rate has over money stock. The other effect, which is the one this paper is concerned with, is the effect over the goods market. The direct effect yields changes in investment.

y=C(y-T)+I(y,ⅈ-πⅇ)+G

This view main explain how expectations of the interaction between the nominal interest and the inflation lead investors to the housing market. Such movement of capital inflows helped create the increase in housing prices often cited by Case-Shiller index. More precise, the increase from 2000 to 2006. Some other indexes indicate somewhat similar. The index of monthly housing prices created by the United States Census Bureau has 1991 as year base, and also shows the housing price increase from 2000 to 2006. Graph number three shows the evolution of housing prices for the Census Bureau index. Perhaps the only difference with the Case-Shiller index is a couple of month lagging in the case of the Census Bureau index. Nonetheless, both indexes point out the sharp increase.

Graph #3.

3

That increase in housing demand can also be seen in the number of housing units completed for the same period. Data taken from the United States Census Bureau indicates that (not seasonally adjusted) completion of housing units in the United States also went sharply up during the same period. From 1990 through 2006 completion of new housing buildings doubled. This reaffirm the statement on the housing price increase due to an increase in demand as well as financial capital seeking real sector to invest. Graph number four shows that such increase ranging from 1990 through 2006 was of about hundred percent. In 1991 the number of new housing built in the United States was roughly one million units. The same indicator for the year of 2006 grew up to roughly two million.
Graph #4.

4

Total number statistics of household organized by income are in table #1. Graph #5 shows the aggregate housing building stock in the United States. 47 percent of Housing buildings in the United States are between 60years and 40 year aged. 33 percent of housing buildings are less than 35 years aged. And roughly 20 percent are more than 60 years aged.
Table # 1.
Total number of households by Income (numbers in thousands).
Sum of Less than $10,000 Sum of $10,000 to $19,999 Sum of $20,000 to $29,999 Sum of $30,000 to $39,999 Sum of $40,000 to $49,999 Sum of $50,000 to $59,999 Sum of $60,000 to $79,999 Sum of $80,000 to $99,999 Sum of $100,000 to $119,999 Sum of $120,000 or more
11401 12228 13882 11434 10212 8581 14330 10054 7195 16577

Graph #5.

5

A bigger picture of the correlation between age of housing buildings and household income in the United States id depicted by graph number 6. That graph shows that the big chunk of buildings currently occupied in the United States were built during 1950’s through late 1970’s. Average Households in the United States tend to live in building built during those years. It is also reasonable to think that those same buildings –that same chunk- comprise a great percentage of the Mortgage-backed loans MBLs. If we assume those buildings require repairs and maintenance, we can assume Building Fatigue came into place as a contributing factor for the Great Recession.
Graph # 6.

6

The graph clearly indicates the distribution of occupied buildings by household income. Note that the great concentration of number of buildings are between 1950’s and late 1970’s. Those four columns may shed light into our hypothesis. Roughly 54 million housing buildings may be running progressively into building fatigue. This effect may be exacerbated by household income.

The model:

We look at the reasons why a Mortgage-backed loan MBL may go underwater. As defined by Blanchard, “underwater” means that the amount of the mortgage exceeds the market value of the house. Without trying to contest any of the current explanations about such phenomenon, it is important to consider how age of buildings combined with household income may have an effect on payments default. In the aggregate, we claim that the United States is currently in transition for renewing housing building stock. That is, more than half of the housing stock has aged over 40 years. Such condition makes structural aspects of the buildings deteriorating. Such effect may affect expectation of the mortgage-backed loans.
Therefore, we claim that there is a higher risk of defaulting based on buildings fatigue. We call that risk “Mortgage defaulting Risk derived from housing building fatigue”. We would argue that the defaulting risk from building fatigue is a function of Household Income (X); plus the age of the house (y) times a ratio of the house price relative to the amount of debt or Mortgage-Backed loan.

f(d)=βx+∂y(Avg Housing Price/Avg Mortgage loan)

If we consider a fact that low income people live in aged housing buildings, it is feasible to start to correlate some more aspects of such stylized fact. Our second hypothesis claims that mortgage loans go underwater more often as the housing building gets older, basically because aged buildings require more repairs than newer housing buildings. The empirical foundations for intuitively thinking this is the case are the following: 3% of the total housing building in the United States have moderate structural problem –mostly in kitchens. Data available in the survey for 2010 shows that roughly 3,939,000 building in the United Sates have problem of such type. A lower number, but in bigger trouble are 1,950,000 housing unit that count on severe physical problems. This are basically plumbing related deficiencies. It makes up to 1% of the total housing buildings. 1% may not sound alarming, but it certainly adds up, especially when such problems are coupled with signs of mice in buildings up to a 9%; and also signs of cockroaches of about ten percent of the total housing buildings in the United States.

This combination of factors is what we call the Housing building fatigue. Living condition after housing purchase may change rapidly creating investment depreciation. The economic activity known as housing flipping may be inflating expectation on the borrower’s side. Renovations performed in old building may increase temporarily the value of the property. In order to keep up with the purchase value, instead of depreciating, the borrower depends on neighbor’s own renovations. A kind of externality will have an effect on the initial value at which the borrower purchased the house. If the borrower bought a renewed house, which be older than say 40 year, its price will depends heavily on neighbors’ renovations. If neighbors renew their houses our borrower’s price will at least keep its value. If the neighbors do not renovate our borrower’s house may depreciate, and therefore the mortgage loan may go underwater.

The neighbors’ externality takes place in a housing market populated with housing buildings demanding renovations. That market segment happens to be concentrated at the low income brackets. The real interest rate is the analogues borrower’s property appreciation. Mortgage loans for many families represent the investment of their life. As such, investments are expected to give a return on capital. There is a borrower’s expectation of a future price increase that incentivize the purchase of the house. This is the same incentive that brings capital to the housing market. Thus, we can claim that there is a housing appreciation that mimics the interest rate in terms of the basket of goods –the real interest rate. We actually can call that the real-real-estate interest rate. We just need to change the level price for the average housing price in order to calculate this rate. Unlike the real interest rate, housing appreciation need not to follow investor’s decision on whether to hold bonds or equity, because the investment is made not necessarily for extracting profits. Nonetheless, this type of investment still requires to show some return, otherwise it goes underwater. As we said we just need to replace the price level (p) from the real interest rate formula.

(1+r_t )=((1+i ̇_t ) p_t)/(pe_(t+1) )

Replacing (P) with the average house price,

(1+r_t )=((1+i ̇_t ) 〖AVG House price〗_t)/(AVG House Price e_(t+1) )

Empirical testing of the first hypothesis:

Hypothesis number one: low income Americans tend to live in aged housing buildings.
We found not a strong correlation among the two variables. However there is a slight sign of such correlation that can get stronger as data allows for more observations and comparisons. That is, low income Americans tend to live in aged housing buildings.

We break down the data of income into tens brackets: less than $10,000; from $10,000 to $19,999; from $20,000 to $29,999; from $30,000 to $39,999; from $40,000 to $49,999; from $50,000 to $59,999; from $60,000 to $69,999; from $70,000 to $79,999; from $80,000 to $89,999; from $90,000 to $99,999; from $100,000 to $109,999; from $110,000 to $120,000 and over. We also break the housing age by fifteen brackets ranging from houses built from 1919 or earlier to houses built in 2013.
The first bracket data depicted in Graph # 1 of the annex shows a correlation between household income and year built of the building they live in. The correlation is a direct negative correlation telling us that it is unlikely to find household income than less than ten thousand dollars living in buildings built the last decade. This correlation gradually inverses as income rises. The highest bracket of household income which for this regression is $120,000 and over gives a positive correlation unlike the lower income bracket. This basically let us generalize –with some caveats- some of the results for the hypothesis. Such conclusion is that low income people tend to live in old houses, whereas high income people tend to live in newer building houses. More details can be seen in the following regressions. As income increase chances to find newer buildings occupied by those households rise either. The threshold where the correlation starts to be positive in at income level $80,000 and higher (Graph # 10 of the annex). Form that level of income it is likely to find such households living in newer building houses. The correlation and the r^2 become higher as income rises.

Conclusions:

We proposed that a contributing factor of the origin of the Great Recession can be what we identify as: default risk in mortgages payments for low income Americans due to a housing building fatigue. In this first article we established empirically that there is a correlation between low income Americans dwelling in aged housing buildings. This correlation makes up just a first step in a series of hypothesis testing regressions in which the general hypothesis will be tested.
Second, by looking at the descriptive statistics we could state that the number of housing buildings in the U.S. were built between 1950’s and late 1970’s, which helps support intuitively our hypothesis. More precisely, 47 % of Housing buildings in the United States age between 60 years and 40 year; 33 % of housing buildings are less than 35 years aged. And roughly 20 % are more than 60 years aged.

Third, this article could also show data that confirm comparable findings of the Case-Shiller index. That is, the increase in housing demand could also be seen in the number of housing units completed for the period ranging from 2000-2006, which allowed us to reaffirm that housing price increased due to an increase in demand as well as financial capital seeking real sector to invest.

Fourth, leverage, complexity, and liquidity in the financial and banking sector may help explain how the crisis spread and how it was amplified. However, looking at the aspects related to housing age may help understand better how the housing sector affects consumption via housing repair needs. Furthermore, housing building conditions may shed light into an explanation of why mortgages go underwater since roughly 5% of the existing housing buildings in the United States have reported some sort of structure deficiency. Certainly, we cannot conclude so far that the age of the house and its neighbor’s effect may exacerbate the risk for mortgage payments defaults, but we can intuitively point out at that as a possible factor for increasing the risk.

Neither can we claim that renovations performed in old building may increase temporarily the value of the property, nor prices of these houses depend on neighbors’ renovations. We are certainly far from proving the risk of defaulting in a mortgage increases with the age of the house, and that housing prices depend on the pace at which old house buildings are renovated.
Annex:
Graph # 1
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References:

Blanchard, Olivier, & Johnson, David (2013). Macroeconomics. Pearson. Sixth Edition.
Kalemi-Ozcon, S. & Sorensen, B. (2011). Leverage across firms, banks and countries. NBER, Working paper # 17,354.
Horton, B. (2013). Toward a more perfect substitute: how pervasive on the issuers of private-label mortgages-backed securities can improve the accuracy of rating. Boston University Review.

U.S. Construction Put in Place Survey: 900,824 millions of dollars.

The Census Bureau released today September 3rd its monthly Value of Construction Put in Place Survey. The total value put in place reached 900,824 millions of dollars, of which 631,403 were spent by the private sector and 296,422 by the public sector. The estimated residential value for the month of July 2013 was around 334,578 million while non-residential was 296,824 millions of dollars. Public sector construction spending went down by 3.7 percent change from July 2012 and 0.3 from June 2013. State and local public organization put 245,381 million of dollars, and the federal government 24,041 dollars which means a -10.2 percent change from July 2012.

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