If you have a moment to read today I have created a throwback post for you. This was my thesis for my MBA at Lincoln Memorial University. It covers the relationship of the Federal Funds Rate and Housing Starts. Since the US economy had the first rate increase in almost a decade in December of 2015 I feel this work is as relevant now, as ever. Also since every new housing start should require a REScheck I feel that if you could successfully predict housing starts, you could accurately predict REScheck reports. Enjoy your read and let me know if you have any questions. [email protected]
Federal Funds Rate Relation to Housing Starts
Jobe David Leonard
I dedicate this in depth look at our nation’s building blocks to Tennessee Technological University, Hogeschool Zuyd, and Lincoln Memorial University. To those instructors who challenged, inspired, and enlightened me and my mind.
|4||Federal Funds Rate||29|
|5||Value of Single Family Construction||35|
|11||Data Gathering Procedure||69|
|15||About the Author||151|
Federal Funds Rate Relation to Housing Starts
Federal Funds Rate Relation to Housing Starts
This work reviews factors that affect housing starts, and determines their effects on the U.S. housing market. The change in Federal Funds Rate, the value of residential construction, personal income, household formation, and population data is analyzed to provide an accurate view of what housing starts have done in the past. This analysis provides a model that can predict residential housing starts. By analyzing monthly data from a fourteen year period this study established that there is some relationship between housing starts and the Federal Funds rate. More specifically, when the change in the Federal Funds rate is negative then housing starts are expected to rise. Inversely when the change is positive then a decrease in housing starts can be expected.
The change in personal income is statistically significant and a positive change in will also stimulate housing starts. If the change in personal income decreases then the study shows that housing starts will decline. The same holds true for household formations and the value of residential construction. In periods when these variables experienced a positive change then housing starts also increased. When the same variable trended downward then housing starts declined.
Looking at what housing starts and the chosen independent variables have done over the past fourteen years identified many key trends. These can be reviewed and compared to different time periods to predict what housing starts will do in the future. These housing starts are an essential component of the United States’ economy on both a micro and macro scale.
Does the change in the number of housing startups per month over a fourteen year period correlate with the changes in the Federal Funds Rate?
Purpose of Study
The purpose of this study is to determine whether the changes in the Federal Funds Rate influences the level of monthly housing starts over the past fourteen years. In addition, this research is undertaken to see what similar indicators encourage increases and decreases in residential housing starts.
Statement of Hypothesis
Hn: The number of housing starts is not related to the level of the Federal Funds Rate.
Ha: The number of housing starts is related to the level of the Federal Funds Rate.
This is a postpositive study that measures the effects of the changes in the Federal Funds Rate, household formation, personal income, and the value of residential construction on residential housing starts. The information was analyzed using time-series data.
Over the past decade, new housing starts have boomed and burst in different quarters and geographical sections of the country. This is driven by many factors that have unique characteristics and influences. These factors affect how willing consumers are to create their own living environment, and the amount of new construction that can be expected. Businesses that provide services and materials for new construction projects require careful planning to decide what resources they will need to acquire or divest in upcoming quarters to ensure profitability. Predicting when these fluctuations happen will provide both homebuilders and buyers with a preliminary indicator of what to plan for in periods of growth and decline.
The data used in this study is noted for accuracy and reliability. Researchers found that, “[t]he Housing Market is an attractive candidate for studying investment behavior because housing construction is highly volatile and the data are among the best available” (Topel and Rosen, 1988, p.718). Housing starts are considered an investment mainly because of the relatively large amounts of money it requires to “start” a house. The size of the house and the market will also be discussed in relation to how starts will affect the entire economy. Haqrylyshyn (1976) stated “[e]ven in an economy such as the United States the value added generated by the home sector seems to account for over one third of the output produced at the market” (as cited in Gronau 1980). Looking at what helped and hampered U.S. housing starts in the past decade, and comparing the results with data from times with similar and dissimilar conditions will allow us to establish parallels in the behaviors of the U.S. consumer to interpret how, and when, they choose to build. This study attempts to explain why residential construction startups occur by analyzing the Federal Funds Rate, population, value of residential construction, personal income, and household formation to identify why residential housing startups happen.
It is time to break ground on a residential construction project, and the start of excavation is considered a housing start. The point in time has been chosen to begin the construction, but “why now?” This could have easily taken place last month or during any month in the future. The fact that the startup has taken place means that the construction phase underway. A residential construction expert explains, “[t]he construction phase is an exciting part of the process. Nearly every day you can see your project take shape with new materials and tradesman arriving at the site to assemble your new home” (Preves, 2001, p. 6). Each worker who contributes labor to the project, and every supplier who provides materials must be paid in a timely fashion and their costs accounted for. The amount of expenses incurred from suppliers and tradesmen will affect the total economic impact of a residential construction start. The Wyss study described this relationship as, “The weaker housing market will affect the economy in two ways: First, falling construction activity will have a direct negative impact. Second, higher mortgage rates and an end to strong home appreciation will restrict Americans’ ability to take cash out of their homes” (Wyss, 2006, pna). The availability of funds is essential when beginning a project, and will be considered.
Reviewing the residential housing starts and
comparing them with independent variable data from each month over the past fourteen years will accomplish this. Then we establish which periods were lucrative for residential construction starts, and which variables were highly correlated with these favorable conditions.
Federal Funds Rate
The Federal Funds Rates is important when building a home that will not be financed with one hundred percent cash. The additional money that is needed must be borrowed and will be repaid with a certain rate of interest. Therefore a preliminary indicator of interest rate movement is ideal when analyzing home starts. The Federal Funds Rate sets a price floor that determines the minimum price of money for a large constituency of financial institutions. This price is set on a nightly basis, and “…is the interest rate that banks with excess reserves at a Federal Reserve district bank charge other banks that have a need for overnight loans” (Itlotus, 2003, np). The price a financial institution pays to borrow money will be highly correlated with the actual interest rates charged to finance future residential construction projects. There are many choices when looking for interest rate indicators to use in a study. The Federal Funds Rate was chosen because of its’ predictive qualities for all other interest rates. Bernanke and Blinder stated, “The federal funds is markedly superior to both monetary aggregates and to most other interest rates a forecaster of the economy” (Bernanke and Blinder, 1992, p. 903). The housing market has the ability to slow down or stimulate the rest of the economy. The inability of an economy to trigger housing startups must be considered when reviewing the short and long-term effects of sluggish construction. “Interest rates are said to affect housing directly by changing either the rate of increase or the volume of deposits at the principal mortgage lending institutions” (Meltzer, 1979, p. 79). The difference in these deposits and the required cash levels set by the Federal Reserve encompass the money that must be traded at the ever-changing Federal Funds Rate.
In contrast, another prominent housing researcher stated that, “It is difficult to find any logical causation between credit and household formation (Topel and Rosen, 1988, p. 718). The Federal Funds Rate might have a direct effect on residential construction startups in this study, but other experts disagree. One thing that everyone can agree on is that the Federal Funds Rate can cause changes to the inflows and outflows of money that can be used to finance residential construction.
Therefore, the increase in construction cannot be ignored in quarters when the Federal Funds Rate was the lowest. One study showed, “In mid-June of 2004, when rates on a 30-year mortgage sank to 5.21 percent, the lowest level in more than 4 decades, housing starts surged to an all-time high of 1.95 million units”(Su, 2005, p. 10). A drop in interest rates coupled with a large increase in housing starts seems to indicate a viable relationship. When the Federal Funds rate is set the decision to begin a project now belongs to the residential construction consumer, and they must choose whether is best to begin construction, postpone building until the next quarter, or not build at all.
Value of Single Family Construction
Housing starts, by themselves, could be considered minimal in relation to their portion of the GDP, but when an indicator is used that includes everything like appliances, furniture, and fixtures. A project needs will be completed when it is considered a “livable” home so it broadens the scope, to include more than just housing starts. It is not logical for someone to build a new home with a two-car garage that they do not intend to place two cars in, or build a kitchen and not purchase a stove or refrigerator. The assumption is made that when certain areas of the home are built that they will ultimately require specific costs to finish. The Value of Residential Construction accounts for these purchases, and encompasses everything it takes to consider the home inhabitable. The size and value of what it takes to make a structure able to be lived in is used an indicator of how much the economy can expect to receive from the additional purchases that assist in transforming the materials, services, and dollars into a home. The Value of a Residential Construction is used as an indicator of how much was spent creating private housing, and what will be spent in the next period.
It does not take any considerations of the quality of what is being built, how long it will last, or how it will be depreciated. If the Value of Residential Construction increases, this does not mean that all homes constructed during that period are exceptional, so consider what a study from 1942 on low cost housing determined, “[i]t stands to reason that the more a family can afford to spend for its housing the better the home it will occupy” (Wittacusch, 1942, p. 350). The cliché’ “You get what you pay for,” was relevant sixty years ago, and applies to housing startups today. Building one ten million-dollar house with the best materials and craftsmanship is identical to building one hundred, one hundred thousand dollar houses with questionable materials and suspect services. This variable will focus on what is being spent each quarter regardless of quality. This will determine if the Value of Residential construction is correlated with housing starts. The total amount that is spent on residential construction will allow us to decide which characteristics of the study were significant in contributing to the results.
A rapid increase in wages could mean that everyone will feel more financially able to change their current housing situation. Also the opposite could take place in an economy with diminishing wages. One study concluded that, “[w]hen the consumer suffers financial distress where he cannot readily pay his bills, he would prefer holding highly liquid financial assets rather than the illiquid housing asset which would be costly to sell in an emergency”(Kearl and Mishkin, 1977, p. 1573). This indicator will provide a snapshot of where the incomes in the United States are positioned. Income will be a preliminary indicator of how much money an individual possesses to build a first home, second home, or increase the size of an existing home. The amount of a person’s disposable income can create or eliminate the ability to undertake a residential housing construction start, or purchase new construction from a homebuilder. An analysis of consumer sentiment and housing starts noted, “[b]ecause housing is typically financed from permanent income, relatively small changes in expected income over the consumer’s life-cycle may have a large impact on housing choice in the current period” (Weber and Devaney, 1996, 343). This impact on choices will be measured using personal income to predict housing starts for each quarter. Reviewing this data and comparing it with related variables, will allow this variable to contribute significant information to the residential construction analysis.
Consider the following, Births, marriages, deaths, and many other circumstances can lead to changes in the numbers of household formations from one month to the next. This study looked at the options that these new households have for shelter and which conditions are attractive to new housing starts. An overpriced housing market can be a lucrative opportunity to build, resale, or form a household, and it could force a new household to rent until the prices stabilize. An under priced housing market could force consumers to stay in existing homes and reconsider previous decisions to begin new construction or rent. A study from the mid 1980’s during a period of rapid residential construction concluded that, “[r]ental housing still plays a major role in the United States, sheltering over one-third of all households”(Downs, 1983, p. 77). When a person rents a home they generally remove themselves from the housing start market for the period of their lease or contract (short term). Therefore new household formations might not always correlate directly with a housing startup due to the many options available in the housing market.
Another option for a new household is to buy an existing home. Now the period of time they are removed from the housing market increases dramatically. Newly formed households can be influenced to believe that it “is” or “is not” attractive to build. This decision may create demand for other forms of housing. New households would also frequently consider what is available on the market. One study found, “Ignoring the role of inventories of unsold new homes can provide an incomplete and misleading picture of the producers’ decision-making problem and, consequently, its solution, that is, housing supply dynamics” (Falk and Lee, 2004, p. 645).
The final option is to undertake a project and create a housing startup. This study shows that an increase in households in combination with the other factors described in this study create an atmosphere that encourages housing starts. Also recognizing that choosing to rent, buy, or build each month might all be viable options for the newly formed household. Reviewing which periods were conducive to home starts and placing it alongside the change in the number of household formations will decipher the months that home starts were more attractive than purchasing an existing home, or renting.
The people that make the United States’ population are the key consumer of new residential construction and a determinant of construction starts. Since the United States currently has a population with a positive replacement rate, the population continues to grow, and so should the need for housing. One would think that a growing population would increase the need for new housing starts. However, previous research states that, “[t]he population growth that maximizes consumption is a stable population” (Weil, 1999, p. 252). This is because a rapid increase or decrease in population has the potential to destabilize other indicators such as income and housing prices, and thus creating a housing market environment that is hostile towards new home construction.
This analysis will look at population and housing formation patterns during the past fourteen years, and attempt to understand what population variances mean in regards to Housing Starts. Smith and Rosen stated, “[h]ousing stock is a capital good with an extremely long life” (Smith and Rosen, 1988, p. 34). This means that existing homes can satisfy a majority of the demand created by light to moderate population growth without a struggle. A home is a long term asset and its’ utility remains until it is torn down, demolished, or considered unlivable. If the population sharply declines, there would be a surplus of existing homes with a life span that could potentially make housing starts less attractive, and therefore housing starts would decline. On the other hand, a decline in population could give acreage that has not been on the market for years, the opportunity to be developed. This available land could be fertile ground for a new subdivision or development on land that was once off limits to the housing markets and housing startups.
Population can fluctuate over the course of fourteen years, and the effects of these fluctuations will be measured and identified by in this analysis. Simon’s study described the relationship as, “[m]ore people, and higher income, cause problems of increased demand and consumption in the short-run. Heightened demand causes prices to rise. The higher prices present opportunity for businesses to make money and for investors to gain satisfaction and glory with new inventions, prompting investors and entrepreneurs to search for solutions” (Simon, 1996, p. 382). These solutions are created as the housing market reacts to population changes and household formations. The results will allow this research to analyze the relationship to the other variables and measure the influence on housing starts. This will provide a reason why residential construction happens, and identify how much of that is related to the constantly changing levels of population and household formations in the United States.
Home building is affected by the climate, and the weather varies in the United States according to time of year and the geographical location. A consideration will be made for the differences in construction during the four generally accepted seasons, which would undoubtedly cause a dramatic drop during winter months and possibly skew the study. One study found, “In analyzing the effects of unseasonable increases in precipitation and temperature or housing starts and completions in the northeast, north central, south and west regions, it was found that unseasonable weather has a substantial impact on housing starts and completions” (Coulson and Richard, 1996, p. 179). Colder weather mainly hinders construction due to the length of time added to the drying time of concrete during colder weather, and the physically demanding work environments that are created by cold weather. This can severely slow housing starts during winter months and this is reflected best in Figure one on page twenty.
Analyzing which customers are able to build each quarter provides a definite competitive advantage. This can be used to plan for seasonal fluctuations. A similar study indicated, “[q]uarterly starts data exhibit enormous seasonal variations. Summertime construction activity is twice as large as in winter (Topel Rosen, 1988, p. 718).” Seasonal differences are reflected in all construction data and this study recognizes this accordingly. This study shows and unbiased seasonal a representation of what will cause housing starts. Then it analyzes monthly data from the past fourteen years to determine which independent variables had the greatest influence.
My model for the experiment is Y= β0 + β1×1 + β2×2+ β3×3+β4×4+ Σ. Y is equal to the change in number of monthly housing starts for the past fourteen years. X1 is equal to the change in the Federal Funds Rate with a two-month lag. X2 represents the monthly change in personal income. X3 is an indicator of the Value of private residential construction put in place each month. X4 shows the change in monthly household formations with a six-month lag. The six figures beginning on page twenty will show a charted description of several samples over the life of the study.
Data Gathering Procedure
The information used for this experiment is derived from data collected from the United States Census Bureau. Information was obtained using the internet and recorded on to spreadsheets. This allowed me to access over 160 different data points for each variable. From this data the study was able to determine whether the Federal Funds Rate and other independent affected the number of housing starts per month.
Figure 1 shows the number of monthly housing starts over a 14 year period.
Figure 2 shows the monthly change in housing starts over a 14 year period.
Figure 1 The seasonality from year to year is evident with significant increases starting in 2002 in combination with a historically low Federal Funds Rate.
Figure 2 Major changes occur in the spring of each year. This creates a positive increase in housing starts that due to seasonal issues. More recently large decreases have been seen as the interest rates climb and the real estate market cools.
Figure 3 shows the monthly Federal Funds Rate over a 14 year period.
Figure 3 The Federal Funds Rate over a 14 year period has remained relatively stable. The exception is noted after September 2001, but gradually began to rise closer to the mean in the middle of 2004. In the analysis decreases in the Federal Funds Rate are shown to cause increases in housing starts as the cost of capital to anyone looking to borrow would decrease significantly.
Figure 4 shows the monthly change in the Federal Funds Rate over the 14 year period.
Figure 4 The monthly change in the Federal Funds Rate shows a relatively stable pattern. Since the Federal Funds Rate is decided on a nightly basis by Federal Reserve Banks. Any increases and decreases can signal changes in many other types of other interest rates. Since the Federal Funds Rate is decided on a nightly basis by Federal Reserve Banks.
Figure 5 shows the monthly Household Formations over a 14 year period.
Figure 5 Monthly Household Formations have shown a gradual trend up over a 14 year period with the only decline after a sharp surge in late 2001. This was caused by a large increase, following September 11th, and a relatively large adjusting decrease to bring the number closer to mean.
Figure 6 shows the monthly change in Household Formations over a 14 year period.
Figure 6 Monthly Change in Household Formations has generally been positive with few periods seeing less Household Formations than the period before. These changes have been shown to cause increases in the amount of residential housing starts.
This analysis used all of its data from information collected from the United States Census Bureau. It was measured monthly from January 1993 to December 2006. Lags were used on two variables to allow the changes in the Federal Funds Rate (2 month) and the changes in Household Formation (6 month) to be more accurately represented. Different models and specifications were used to determine the most appropriate number of lags for these independent variables in the model.
F test: The F test is 20.69, which was taken from the ANOVA table. This table gives a summary of the analysis of variance. To determine whether or not the regression analysis is significant. Ho: ß1 = ß2 = ß3 0; Ha: At least one of the Betas is equal to zero. The F value in the numerator with 4 degrees of freedom and 156 in the denominator is F.01 = 2.42 since the computed F is 20.69, which is larger than 2.42, the model is acceptable. This model explains some of the variability in the dependant variable
R²: Multiple coefficient of determination is a measure of the goodness of fit for the projected regression equation. The goodness of fit test is a statistical testing procedure for determining whether or not to reject a hypothesized probability distribution for a population. In my analysis, the R² = .346, which means that 34.6% of the variability in housing starts are clarified by the variability in my independent variables.
T test and p-value: In the regression output chart the t statistic for the lagged Federal Funds Rate change is -1.84. To determine if the t statistic is going to support the null hypothesis or not, at a 95% confidence level, I find the t value (156 df) = +- 1.31 and reject the null –t .025 ≤ t ≤ t .025. Since t = -1.84 < t = -1.31, the null hypothesis is rejected, and I conclude that the number of housing starts are negatively affected by the change in the lagged Federal Funds Rate.
By rejecting the null hypothesis, the alternative hypothesis is accepted. The factors that affect the number of housing starts include: the change in the Federal Funds Rates with a 2 month lag has a negative coefficient of –10 so if the lagged Federal Funds Rate were to rise then one could expect the number of housing starts to increase. Inversely when the Federal Funds Rate drops a then an slowdown in housing starts could be expected. The change in personal income shows a positive coefficient of .038. This indicates that any monthly raise in the populations’ income has the ability to positively influence housing starts. In contrast a drop in the amount of personal income would have a negative effect on housing starts. The value of new single-family private residential construction shows the most statistical significance in its’ t-score, but it shows a positive coefficient of .005. This is extremely close to zero but remains positive. This means that an increase in residential construction from the month before will mean that housing starts will increase also. A decrease would indicate that housing starts could go down. Finally, the six-month lag of the change in household formation shows a positive coefficient of .004 meaning those periods when the number of households formed changes positively then there is an expectation that housing starts will increase also. When the change in number of household formations in down then housing starts may trend downward.
Using the knowledge presented above can provide a forecast unrivaled in previous scholarly research. One must recognize that all independent variables move independently, and do not have to move in the same direction to change housing starts. It is shown that housing starts increase as the Federal Funds Rate goes down. Also an increase in personal income, private residential construction, and household formations can positively affect the change in housing starts. Any or all of these conditions provide an ideal environment for housing starts.
When the opposite changes to the independent variables occur a drop in housing starts is expected.
By monitoring current data it is possible to predict were housing starts will fall in the periods to come. It also provides an innovative analysis of a relevant market that has not been given the due focus in recent years. This study uses data that is available each month and can predict the future housing starts almost instantly by reviewing the changes in the independent variables. This can provide insight into what will happen in the construction industry to economist and homebuilders alike.
The purpose of this study was to determine what factors affect housing starts in the United States. The Federal Funds Rate, the change in personal income, the value of private residential construction, and household formation all show some correlation with the amount of housing starts that occur each month.
This study shows that when one variable decreases and the other five increase it is it could mean an increase in housing starts. For this to happen the Federal Funds Rate must drop followed by an increase in personal income, the value of private residential construction, and household formation. This analysis shows that changes in personal income is highly statistically significant with the numbers of housing starts. Population did not show a strong correlation with number of housing starts per month.
This study’s coefficient of the determination (R²) of 34.6 is not the greatest goodness of fit test, but should be acceptable, because this study is dealing with real world data. The Federal Funds Rate, changes in personal income, value of residential construction, and all show a relationship with the number of housing starts that occur each month.
Based on the model and the current level of the Federal Funds Rate can be forecasted that housing starts may continue to slow down for the next four to five year period. This is predicted because of relatively low numbers of new residential construction, and marginal increases in personal income and household formations. Furthermore, current inventories of existing homes, oil prices, and predatory lending practices may continue to pull down the housing market and starts for many periods to come.
|Adjusted R Square||0.329929|
|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%||Lower 95.0%||Upper 95.0%|
|(X1) 2 month Lag Change of Federal Funds Rate||-10.0641||5.467968||-1.84056||0.067||-20.86||0.736||-20.8649||0.736695|
|(X2)Change in Personal Income||0.038048||0.018745||2.029832||0.044073||0.001022||0.075||0.001022||0.075074|
|(X3)Change of Value of private residential construction put in place||0.005262||0.000594||8.852929||1.77E-15||0.004088||0.006||0.004088||0.006436|
|(X4) 6 month Lag Change of Household Formation||0.004209||0.002932||1.435397||0.153176||-0.00158||0.01||-0.00158||0.01|
This analysis satisfies the assumptions for a Multiple Regression Analysis. Each of the cases are independent, they show normality, and the variance is constant.
(X1,X2)=.014 (X1,X4)=.008 (X2,X4)= .018
(X1,X3)=.033 (X2,X3)=.034 (X3,X4)= .58
Federal Funds Rate- “This is the interest rate that banks with excess reserves at a Federal Reserve district bank charge other banks that need overnight loans. The Fed Funds Rate, as it is called often points to the direction of interest rates, since it is set daily by the
market, unlike the prime rate and the discount rate”(Itlocus, 2003, np).
Household- A household consists of all the people who occupy a housing unit. A house, an apartment or other group of rooms, or a single room, is regarded as a housing unit when it is occupied or intended for occupancy as separate living quarters; that is, when the occupants do not live and eat with any other persons in the structure and there is direct access from the outside or through a common hall. A household includes the related family members and all the unrelated people, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. There are two major categories of households, “family” and “nonfamily” (U.S. Census Bureau Definitions, 2006).
Housing Market- “(1) newly constructed single-family houses not yet sold or occupied, (2) new rental units, (3) previously occupied units being offered for resale, and (4) previously occupied units offered for rent” (Naylor, 1967, p. 384).
Housing Starts- The start of construction is when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when excavation for the building has begun. Beginning with statistics for September 1992, estimates of housing starts include units in residential structures being totally rebuilt on an existing foundation. Housing starts are estimated for all areas of the United States, regardless of whether permits are required. (U.S. Census Bureau Definitions, 2006).
New Residential Construction- The category of statistics called “New Residential Construction” consists of data on the five phases of a residential construction project. This is housing units authorized to be built by a building or zoning permit; housing units authorized to be built, but not yet started; housing units started; housing units under construction; and housing units completed. New residential construction statistics exclude group quarters (such as dormitories and rooming houses), transient accommodations (such as transient hotels, motels, and tourist courts), “HUD-code” manufactured (mobile) homes, moved or relocated buildings, and housing units created in an existing residential or nonresidential structure. However, in a new building combining residential and nonresidential floor areas, every effort is made to include the residential units in these statistics, even though the primary function of the entire building is for nonresidential purposes. These statistics only include privately-owned buildings. Publicly owned housing units are excluded from the statistics. Units in structures built by private developers with partial public subsidies or which are for sale upon completion to local public housing authorities under the HUD “Turnkey” program are all classified as private housing. (U.S. Census Bureau Definitions, 2006).
Residential Building- A residential building is a building consisting primarily of housing units. In a new building combining residential and nonresidential floor areas, every effort is made to include the residential units in these statistics, even though the primary function
of the entire building is for nonresidential purposes. (U.S. Census Bureau Definitions, 2006).
Arcelus, F., Meltzer, A. (1973). The Markets for Housing and Housing Services, Journal of Money, Credit and Banking, 5, 78-99. Retrieved October 12, 2006 from Business Source Premier database.
Austin, J., Burnham, J., Maisel, S. (1971). The Demand for Housing: A Comment, The Review of Economics and Statistics, 53, 410-413. Retrieved September 15, 2006 from Business Source Premier database.
Bernanke, AuthorBen S., & Bilnder, Alan S. (1992). Credit, Money, and Aggregate Demand. The American Economic Review. 82 No. 4, 901-921.
Campbell, J.M. (1978). Aggregation Bias and the Demand for Housing, International Economic Review, 19, 495-505. Retrieved October 5, 2006 from Business Source Premier database.
Coulson and Richard, (1996).The dynamic impact of unseasonable weather on construction activity. Real Estate Economics. 24n.2, pp179. Retrieved October 21st from Infotrac online database.
Devaney and Weber , (1996).Can consumer sentiment surveys forecast housing starts?. Appraisal Journal. 64, p. 343. Retrieved October 25, 2006 from Infotrac database.
Downs, A. (1983). Housing America, Annals of American Academy of Political and Social Science, 465, 76-85. Retrieved October 4, 2006 from Business Source Premier database.
Ellwood, D., Polinsky, A. (1979). An Empirical Reconciliation of Micro and Grouped Estimates of the Demand for Housing, The Review of Economics and Statistics, 61, 199-205. Retrieved October 14, 2006 from Business Source Premier database.
Falk and Lee, (2004).The inventory sales relationship in the market for new single-family homes. Real Estate Economics. 32.4, 645.
Fallis, G., Rosent, K., Smith, L. (1988). Recent Developments in Economic Models of Housing Markets, Journal of Economic Literature, 26, 29-64. Retrieved October 3, 2006 from Business Source Premier database.
Fisher, R. (1969). Monetary Policy: Its Relation to Mortgage Lending and Land Economics, Land Economics, 45, 418-424. Retrieved September 17, 2006 from Business Source Premier database.
GlossaryItLocus. Retrieved October 23rd, 2006, from Definitions Web Site: http://glossary.itlocus.com/federal _funds_rate.html.
Gronau, R. (1980). Home Production—A Forgotten Industry, The Review of Economics and Statistics, 62, 408-416. Retrieved October 7, 2006 from Business Source Premier database.
Herzog, J. (1963). Structural Change in the Housebuilding Industry, Land Economics, 39, 133-141. Retrieved September 29, 2006 from Business Source Premier database.
Hull, G. (1931). The Insecurity of the Industry, Annals of American Academy of Political and Social Science, 154, 148-152. Retrieved October 7, 2006 from Business Source Premier database.
Kearl, J.R., Mishkin, F. (1977). Illiquidity, the Demand for Residential Housing, and Monetary Policy, The Journal of Finance, 32, 1571-1586. Retrieved October 7, 2006 from Business Source Premier database.
Maisel, S. (1949). Have We Underestimated Increases in Rents and Shelter Expenditures?, The Journal of Political Economy, 57, 106-117. Retrieved September 8, 2006 from Business Source Premier database.
Maisel, S. (1963). A Theory of Fluctuations in Residential Construction Starts, The American Economic Review, 53, 359-383. Retrieved September 15, 2006, from Business Source Premier database.
Manchester, J. (1988). The Baby Boom, Housing, and Financial Flows, The American Economic Review, 78, 70-75. Retrieved September 10, 2006 from Business Source Premier database.
Naylor, T. (1967). The Impact of Fiscal and Monetary Policy on the Housing Market, Law and Contemporary Problems, 32, 384-396. Retrieved October 16, 2006 from Business Source Premier database.
New Residential Construction. Retrieved October 29, 2006, from U.S. Census Bureau Web site: http://www.census.gov/const/www/newresconstdoc.html#definitions (2006, September 7).
Polinsky, M. (1979). The Demand of Housing: An Emphirical Postscript, Econometrica, 47, 521-524. Retrieved September 13, 2006 from Business Source Premier database.
Pollock, R. (1973). Supply of Residential Construction: A Cross-Section Examination of Recent Housing Market Behavior, Land Economics, 49, 57-66. Retrieved October 20, 2006 from Business Source Premier database.
Preves, R (2001). New House More House. Libertyville, IL: Portico Publishing.
Reichert, A. (2004). The impact of interest rates, income, and employment upon regional housing prices, The Journal of Real Estate Finance and Economics, 3, 373-391. Retrieved September 25, 2006 from Business Source Premier database.
Renshaw, E. (1971). The Demand for Housing in the Mid-1970’s, Land Economics, 47, 249-255. Retrieved October 1, 2006 from Business Source Premier database.
Rosen, S., Topel, R. (1988). Housing investment in the United States, The Journal of Political Economy, 96, 718-740. Retrieved October 1, 2006 from Business Source Premier database.
Shelton, J. (1968). The Cost of Renting versus Owning a Home, Land Economics, 44, 59-72. Retrieved October 14, 2006 from Business Source Premier database.
Simon, Julian L. (1996). The Ultimate Resource 2. Princeton, NJ: Princeton University Press.Su, B (2005).The U.S. economy until 2014. Monthly Labor Review. 128, 10-15.
Weil, David N. (1999).Papers and Proceeding of the One Hundred Elevent Annual Meeting of the American Economic Association. 89, 252.
Weinfied, E. (1949). Can America Be Adequately Housed?, American Journal of Economics and Sociology, 9, 77-84. Retrieved October 17, 2006 from Business Source Premier database.
Wittausch, W. (1942). Used Homes in the Low-Cost Housing Market, The Journal of Land & Public Utility Economics, 18, 350-356. Retrieved September 22, 2006 from Business Source Premier database.
Witte, A. (1977). An Examination of Various Elasticities for Residential Sites, Land Economics, 53, 401-409. Retrieved October 10, 2006 from Business Source Premier database.
Wyss, D. (7/10/2006). Retrieved October 29, 2006, from Business Week Online Web site: http://www.businessweek.com/the_thread/hotproperty/archives/2005/07/past_housing_bu.html
About the author
Jobe Leonard lives in Dandridge, TN. After attending Tennessee Technological University he received his MBA at Lincoln Memorial University. He is a project manager with Hearthstone Homes and has currently built over 100 custom log and timber homes in 26 different states. This includes a recent project he managed that was named the 2012 National Log Home of the Year. In addition, he is the owner of LakeFun.com where he serves as the current CEO of this Internet startup company. He is also a writer, contributor, and content creator for GoOverseas.com. For more information on his current projects, other books, and free worksheets visit www.Jobe.ws.
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