Why Is the Homeownership Rate Still Falling? An alternative explanation.

When it comes to loan rates, the one that concerns the most regular consumers is the mortgage loan interest rate. This past February 2016 a 30-year mortgage interest rate averaged 3.66 percent accordingly to Freddie Mac, whereas the homeownership stubbornly kept its 63 percent level. So, with the mortgage interest rates averaging 3.53 percent (15-year mortgage loan), why the homeownership has not come back to 69 percent level as it was before the Great Recession? Some analysts have proposed cynically that 69 percent homeownership represents an unsustainable level, and that homeownership is no longer attractive. Neither of those explanations would look rational to a maximizing agent. Otherwise, an alternative analysis could lead to a different conclusion. That is, low-interest rates are helping investors to outbid competitors rather than prospective homeowners to get a house. The worrisome part of the problem is that this situation could lead the housing market to a crisis due to inflated home prices, as well as to higher levels of inequality.

Given that purchasing a house represents arguably the biggest investment of a lifetime of a regular person, these rates are mainly observed by monetary authorities, analysts, and homebuyers. In fact, these rates have become even more relevant since the Great Recession originated ostensibly from failures within the regulation of the housing market.

By Catherine De Las Salas

By Catherine De Las Salas

Homeownership rate has been declining.

A rapid view of real estate market indexes will show firstly that homeownership rate is flat. This rate has been flat and declining since its highest level before the Great Recession for which it reached 69 percent. Last economic quarter of 2015, homeownership registered 63.7 percent. Second, prices of both sales and rents are up to the extent that cost of shelter is among the only factors driving up inflation in the United States. Following the Case-Shiller index and the Federal Housing Finance Agency, home prices have increased at a yearly rate of 6.0 percent. Third, new residential sales, as measured by the U.S. Census Bureau, were also up by 6.1 percent in January 2016 when compared to the same month of 2015. Likewise, Pending Home Sales in January recorded 3.5 percent increase. Fourth, mortgage loan interest rate averaged 2.96 percent for a 15-year fixed loan during February 2016 (find more on housing indicators)

Investors could be outbidding prospective homeowners.

All these indexes beg the question on why homeownership has not increased due to the rising levels of sales, as well as the cost of shelter, and upturns in home prices. One of the answers available for this puzzle is that investors are taking over the market. Investors could be outbidding prospective homeowners making it harder for them to access ownership. Likewise, having investors controlling the housing market retains the risk that speculative money could inflate a bubble again in the housing sector, leading loans to go underwater at some point afterwards. A housing sector crisis could repeat under the same circumstances of the Great Recession nowadays.

The counter argument derives from the fact that the housing crisis was only the trigger for the Great Recession to start. Indeed, default in mortgage-backed loans trickled down in the form of multiple spillovers on the banking system. Securitization of banking products through the practice of bundling subprime mortgages led to the spreading of toxic assets all over the financial system (learn more of this issue here). Therefore, the fact that recent regulation within the financial system, as well as regulation governing lending practices, makes it less vulnerable for the rest of the economy. So, if the housing sector happens to be a risky position, an eventual crisis will not spread inasmuch as it did before the Great Recession.

The consequence, rising levels of inequality.

So, although a housing sector crisis could be discarded by looking at the arguments herein, the effects on inequality could not. As of March 2016, there appears to be no worrying signs or data with respect to the housing market. Nevertheless, assuming that homeownership has not increased because an alleged lack of incentives in owing seems ridiculously naïve.  And concluding that pre-Great Recession levels of homeownership were unsustainable appears not rational either. Then, an alternative explanation points at the competition of capital for seizing valuable assets. The consequence, low level of homeownership rate while rising levels of inequality.

Is the Google search term “Dollar Rate” an useful predictor for economic crisis?

Economic conditions in the United States are so unchanging that economists started to explore global conditions as potential threats to its economy. Without ruling out financial institutions altogether, economists are confident current regulation will keep turbulence away. Also, the household sector appears solid for many analysts while fiscal issues seem not to alarm anybody. Nevertheless, the reality is that current economic conditions look much as an economic boom that nobody knows where it is going to burst from. This situation makes economic fears to hide just in front of analysts. Given that the economic expansion that started with the economic recovery from the Great Recession is about to reach six years now, one useful way to try to anticipate ambiguous economic situations could be by looking at Google Trends insights. Mainly, Google searches for certain terms such as “Dollar rate”. However, China’s battle against Google limits the most interesting insight we could possible get from China’s economic situation as it develops.

Factoring in potential risks:

Since economists believe in economic cycles and expansions that last for about ten years in average, the current development should start factoring in potential risks. Thus far, in regards to the American economy, there appear to be two suspicious economic sectors that might be fueling economic anxiety as current expansion continues to grow. On one hand low oil prices are generating a reallocation of resources that is allowing many sectors of the economy to grow. On the other hand, inflows of international capital migrating to the United States could be signaling countries under pressures. The latter issue is where analysts are looking for potential threats. In this article, econometricus looks at data that could hold some clues on where turbulence could be starting to grow abroad. Indeed, one of the Americans’ biggest concerns is about China’s economic performance. The question “is it safe to invest in China?” has popped up as one of the most question asked the last two years, even over concerns about Greece.

Remembering 2015 and Google query “Dollar rate”:

Let us start by remembering that 2015 exhibited panic for international spillovers of default of local economies. Greece had the world wondering who is next, while Greeks looked for currency alternatives (Graph 1). That same question has U.S. analysts looking for potential risks from the international community. Thus, using big data and Google search terms may help researchers to track concerning developments. Indeed, growing interest for exchange rate could hold a clue for analysts as Google searches unveil the geography of those interest.

So, econometricus took the Google query “Dollar rate” as a proxy for the interest in exchanging local currency random people could have. In other words, if people worry about economic condition in a given country, they will try to exchange local currency into U.S. Dollar. Such behavior could unveil early developments in big capital outflows from key trading partners. Therefore, looking at those Google searches might illuminate analysis for identifying potential global threats.

Graph 1.

Dollar rate 3

Graph 2

Dollar rate 4

Although it could be helpful to assume money tenders tend to sell local currency and exchange it for foreign dollars, it is still hard to claim that Google searches are useful predictors for economic crisis. Econometricus does not claim that by any means. However, when complemented with other data, Google searches may help picture a better analysis and, what is more relevant, Google searches could illuminate real time developments. So, let us try to see where Google could take us.

China blocked the sensitive information on Google:

Unfortunately data on Google searches will not take the analysis any where as far as the most pressing issues regards. The most concerning country nowadays, China, blocked the sensitive information on Google. Graph 2 shows how China’s data on US currency searches has been blocked for retrieval since May 2014, which limits this analysis. Nevertheless, Google still offers others country data such as Brazil, which has also been closely watched by analysts. Brazil shows an upward trend in interest for “Dollar rate” term (Graph 2). Instead, Taiwan shows a steady trend (Graph 2). Mexico and Canada, the two biggest trading partners after China, appear to have a growing interest in US dollars (Graph 3).

Graph 3

Dollar rate 2

Graph 4

Dollar rate

Finally South Korea, Germany, and the United Kingdom. Among these three nations, only South Korea shows significant increases in the search term “Dollar rate”. Perhaps the UK may exhibit a bit of interest recently. However, it is not clear right now to what extent it could relate to economic troubling factors. We all wish we can count on China’s data so that the analysis could be expanded properly. Sadly, that is not the case as of March 2016.

Despite job losses, New Jersey’s labor market looks vibrant rather than sclerotic.

Regional and State statistics on employment and unemployment for the month of July 2015 looked motionless for the great majority of States in terms of over-the-month changes. Over-the-year though, nonfarm employment increased in 47 states and deceased in 2. In terms of employment levels, the greatest over-the-month increases were seen in California (+80,700), Texas (+31,400) and Florida (+30,500); while percentage wise, greatest increases were in Wyoming (+0.9 percent), Oklahoma (+0.7 percent), and Rhode Island (+0.7 percent). It is worth noting that a year ago, Rhode Island had an unemployment rate of 7.6 percent, while California’s was about 7.4 percent. Today, those two states reported unemployment rates of 5.8 percent (Rhode Island) while California recorded 6.2 percent.

Unemployment Rate July 2015.

Unemployment Rate July 2015.

Otherwise, declines in employment levels were statistically significant in North Dakota (-0.5 percent), Hawaii, Kansas, New Jersey, and West Virginia with -0.3 percent decline each. West Virginia noted an increase of 1 percent point and registered an unemployment rate of 7.5 percent. Both Dakotas also showed increases in Unemployment rate.

The challenging aspects for the analysis this time stem from the data coming out from New Jersey, Kansas and Louisiana. These three states showed decreases in employment level from June to July 2015. New Jersey’s level of employment decreased by -13,600 jobs, while Louisiana and Kansas did so also by -4,500 and -4,300 respectively. Given that the declines happened during the summer season, they all beg the question on whether those job losses were quits or separations.

When it comes to labor markets, employment levels can have negative variation for two reasons. First, firms may stop hiring new employees and further start firing the current ones. Second, employees may quite their jobs. In order to be accurate, it is key for the analysts to determining under what circumstances the drop in the statistic happened. The most expedited way to find out, whether the job losses were on the firm’s end or on the employee’s end, used to be by looking at Massive Layoff data from the BLS. However, the Massive Layoff program ended since the budget cuts fights in 2013 between Republicans and Democrats.

So, going back to New Jersey’s employment level data for the month of July 2015, intuitively it is hard to believe that a job drop happened in the state during the summer, which only has happened 13 times in almost 40 years –five of which happened since the Great Recession Started-, and it has done so mostly during economic recessions. So, particularly in the case of New Jersey, the question about quits versus separations begs an answer.

New Jersey's Level of Employment Change June-July 1076-2015.

New Jersey’s Level of Employment Change June-July 1976-2015.

Given that there is not Massive Layoff data available, one way to scratch the surface of what is going on in the State’s labor market is by looking at its output and current economic conditions. Indeed, the southern part of the state -Lehigh Valley and the Southern Jersey Shore- have seen a slowdown in real estate markets. The region, which is covered by the Eleven District of Philadelphia at the Federal Reserve System, has experienced moderate to positive changes in the economy through the second quarter of 2015. In particular, regionally speaking, auto-dealers have seen flat sales during the summer. Home builders also reported little change in activity for the same period. Likewise, and although manufacturing picked a bit up, food products, primary metals and electronic products have seen sales decreases. Similarly, staffing firms reported slowdowns as well as trucking activity showed signs of weakness.

Apparently, there is no drama when it comes to assess the current economic condition of the region. Besides the industries cited above, every other sector reported moderate improvements. Thus, the overall economic conditions of the state are not that bad so as to expect such a drop in employment levels. In fact, the State Unemployment Rate has declined since 2009 to 6.5 percent. Right after the Great Recession started, New Jersey’s Unemployment rate was over 9 percent. Even though the state’s labor market recovery appears to be slow, it also looks steady. Therefore, what seems feasible to interpret under the current circumstances is that New Jersey’s labor market looks vibrant rather than sclerotic. That is, workers in New Jersey quitted their job for better opportunities elsewhere.

Who is holding back the housing market?

Housing market slowed down by 3.3% in United States during the month of April 2015, the National Association of Realtors reported on May 21st 2015. Lowest levels of sales by price were on the price range below 100K. However, Realtors are confident the housing market will rebound during the summer as they see April’s slow-down as transitory.
Lawrence Yun, chief economist at the NAR, said on Wednesday that current existing housing April statistics failed to keep pace with the gain seen in March. “April’s setback is the result of lagging supply relative to demand and the upward pressure it’s putting on prices”. Yun statement actually went against the evidence provided by NAR itself, for which the housing inventory increased by 10% to 2.22 million existing homes available for sale. Therefore, someone in between sellers and buyers must be slowing down the housing market.
Although the slow-down may be associated with the business season, there are few economic arguments to justify April’s pace. Furthermore when the interest rate for a 30 years commitment fixed-rate mortgage continued to decline to 3.67% during the same month, according to Freddi Mac. Additionally, the average house price for the single-family home sold in April was $221.200. So, the question we pose is Who is holding back the housing market?

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To learn more on housing market and some of the reasons the housing market is weak in USA.

241,000 jobs added to US Economy in December 2014: ADP.

Apparently, the US economy follows its way into a strong recovery. Employment level closed 2014 with an increase of roughly 241,000 jobs compared to November 2014. New employees went to work mostly for small businesses, which added 106,000 jobs to the economy. Medium businesses added roughly 70,000 whereas large businesses only 60,000. There have not been major weather events in the US affecting employment so far (winter 2014). Thus, ADP statistics show a good path into 2015.
By industry, Services still leads the hiring in the recovery with a total of 194,000 jobs added last month. Construction and Manufacturing added 23,000 and 26,000 respectively. Carlos Rodriguez, CEO of ADP, pointed out that “December delivered another strong number well above 200,000 to close out a solid year of employment growth with over two and a half million jobs added”.

ADP new hires

In terms of the economic recovery from the Great Recession, since early 2014 almost every sector is adding jobs to the economy. The first sector pulling up job creation has been Professional Businesses, which is followed by Trade and Transportation. Although at a slower pace, Manufacturing sector has been also adding jobs since September 2011. The last sector in joining job creation statistics was Construction. Graph # 2 shows percentage change in level of employment indexed to June of 2009 (end of the Great Recession). Mark Zandi, chief economist of Moody’s Analytics, claims, “The job market continues to power forward. Businesses across all industries and sizes are adding to payrolls. At the current pace of job growth, the economy will be back to full employment by this time next year.”

ADP Employmet level gains

Optimism among Moody’s analyst contrasts against economic perspectives of several surveys such as the Texas Manufacturing Outlook Survey (TMOS). Many of the TMOS responders manifested cautiousness, and actually adjusted expectations about future new employees, at least, for the six first months of 2015.

ADP collects data from 411,000 companies for which it manages their payroll information. Those 411,000 companies comprise more than twenty percent of all total US private sector companies.

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:
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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.