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Effects of Monetary Policy on Housing Price Cycles in the US, 1950-2016

The Effects of Monetary Policy on the Dynamics of Housing Price Cycles in the United States, 1950-2016


Housing cycles are a barometer of the macroeconomic situation in the United States, and their supply-side and demand-side dynamics have been discussed in detail in the existing literature. This paper proposes a long-run approach with an emphasis on the demand side and its policy determinants. It traces the empirical connections between the Effective Federal Funds Rate (EFFR), one of the main monetary policy tools used by the Federal Reserve, and the movements of housing prices in the United States from 1950 to 2016, as captured by the S&P Case-Shiller Home Price Index and influenced by other macroeconomic cyclical variables. The paper builds an empirical, long-run model which incorporates the propositions that the movements of the EFFR exhibit strong lagged correlation with housing market cycles in the United States. It also demonstrates that the influence of interest rate shocks on housing markets has been transmitted with a lag of between one and four years and that this lag has been changing as the underlying structure of the US financial system has altered. Finally, the paper proposes a combined approach of monetary policy intervention and macro-prudential regulation to moderate the duration and amplitude of housing cycles and weaken the interdependencies between them and the wider financial system.



Chapter                                                          Page


  1. Introduction…………………………………….5
  2. Theoretical frameworks………………………..7
  3. Existing literature……………………………..10
  4. Empirical discussion……………………………13

1. Empirical process and 1950-2016 VECM model……..13

2. Sub-period VECM models…………………………….18

3. Empirical conclusions…………………………………20

  1. Policy implications……………………………23

1. Monetary policy and asset-market regulation………….23

2. The subprime crisis from a policy perspective…………23

3. Towards a combined monetary-regulatory approach….26

  1. Conclusions……………………………………31
  2. Bibliography………………………………….32
  3. Appendix A – VECM specifications………….35
  4. Appendix B – Data sources…………………..39
  1. Introduction

US housing markets experienced a number of cyclical movements from 1950 to 2016. The last cycle ended in 2007 with the subprime mortgage crisis, causing the deepest recession since the 1930s and substantially affecting the global economy. Identifying the reasons behind US housing-price dynamics and developing empirical models to capture relationships between policy variables and outcomes is key to moderating the macroeconomic effects of housing cycles. Monetary policy is an important determinant of credit conditions underpinning mortgage lending, which supports demand for housing. Understanding the effects of monetary policy on the duration and amplitude of housing cycles, as well as the interaction between monetary policy and financial regulation, is crucial to avoiding a repetition of the subprime crisis.

Central banks hold the monopoly on ‘producing’ money and setting the basic interest rate in an economy, making their influence on credit and capital markets considerable. Asset markets, including housing markets, are significantly affected by credit conditions as many of their transactions are debt-financed. Housing markets are a class of asset markets with singular characteristics. Supply tends to be inelastic, as land supply is usually fixed and urban land needs to be redeveloped for housing. There exists a slow feedback mechanism between market conditions and supply due to the physical time required for construction and frictions in the user market, investment market, and developer market (these frictions are captured in detail in the four-quadrant model of real estate markets proposed by diPasquale & Wheaton, 1992, and by Keogh, 1994, for the UK). Housing markets have high importance in the US political economy, exemplified by the ideas of the home-owning democracy and the ‘ownership society’. Housing is subject to policy intervention and planning restrictions by local, state and federal government. There is high dependence on debt financing both on the supply side (for developers) and on the demand side (for homebuyers) because of the high land, building, and maintenance costs. Debt financing usually comes in the form of mortgage loans that employ the property as collateral. Mortgage financing conditions depend on the lender’s assessment of present and future economic conditions. Factors include: the current value of the property based on the situation in local and national housing markets; the current income status of the creditor (determined by their employment situation in the case of homebuyers and by the cash flow situation in the case of developers); the future value of the collateral; and the future income prospects of the creditor (likelihood of homeowner making loan payments on time for a homebuyer, or the ability of a developer to sell units under construction).

The purpose of this paper is to measure the long-run empirical connections between monetary policy actions taken by the Federal Reserve as represented by changes in the average quarterly Effective Federal Funds Rate and the cyclical movements of the composite S&P CoreLogic Case-Shiller Home Prices Index (an aggregate, indexed measure of the movement of US house prices, with the 1950 level taken to be 100). This effect will be estimated whilst considering other business-cycle variables that might influence the movements of the Case-Shiller Index, including Average Quarterly Unemployment levels in the US, year-on-year quarterly real GDP growth, the Average Quarterly Consumer Sentiment Index published by the University of Michigan, and the Total Dwellings and Residential Buildings Started for the United States Quarterly Index (RBI). The paper will also discuss the evolving understanding in the existing literature of the complex interactions between monetary policy and housing markets, and compare its findings on the magnitude and lag of monetary policy action on housing prices to the results of similar studies.

  1.       Theoretical frameworks

The effects of monetary policy on housing-price cycles are only one facet of the complex relationship between monetary conditions and financial stability. Standard economic theory suggests that monetary policy decisions affect housing prices through their impact on credit conditions in the macroeconomy, given that housing markets are highly sensitive to credit conditions. The impact of monetary policy on housing markets is transmitted through the influence of monetary policy on mortgage rates for house-buyers (affecting demand for housing) and on loan rates for house developers (influencing supply for housing).

In the United States, the Federal Reserve influences credit markets through its open-market operations, which keep the Effective Federal Funds Rate (EFFR) within the target limits set by the Federal Open Market Committee (FOMC). In theory, lowering the EFFR leads to cheaper credit, making housing more affordable. This in turn causes demand for housing to rise, which leads to higher housing prices in the short run, all other conditions being equal and supply being inelastic in the short run and adjusting slowly to higher demand in the long run. Alternatively, acting to increase EFFR leads to more expensive credit and makes homes less affordable, causing lower demand for housing and hence lower prices under the two conditions stated above. In the long run, lower rates should stimulate builders, thereby bringing more supply and reducing prices, and higher rates should do the opposite, taking out supply and raising prices. The relative strength of interest rates pulling demand and pushing supply depends on the relative elasticity of supply and demand with respect to changing interest rates.

It has long been argued in the theoretical literature that housing and land markets are primarily demand-driven due to supply constraints. The classical treatment of the subject (Ricardo, 1821), as well as modern models such as the bid-rent theory of Alonso (1964) give rise to surplus theory, which explains rents and prices as based on demand proxied by the income-generating abilities of the property. Following this theoretical argument, the general expectation should be that higher rates lead to lower house prices, and lower rates should lead to higher prices, an effect that is slightly dampened by the opposite, slower dynamics on the supply side. The latter takes much longer to adjust to changed credit conditions due to the specificities of housing markets discussed in Part I of this paper.

The theoretical approach of this paper to the US housing market follows the model developed by Arestis and Karakitsos (2007), which ascribes greatest importance to the mortgage rate and to real disposable income (in that order) as drivers of housing prices in the United States at the homogeneous, national level. The authors emphasise treating the US housing market as a homogeneous structure rather than as a compendium of regional markets. This model expresses demand for housing as a function of two short-run variables (real disposable income and the mortgage rate), and two long-run factors (the debt service burden and the net real estate of households). Supply for housing is a function of housing prices, the level of housing starts, and the level of real residential investment. In the long run, housing prices are a function of the real disposable income, the mortgage rate, the net real estate of households, the level of housing starts, and the level of real residential investment. The authors stipulate that the level of real disposable income, the debt service burden and the net real estate of households have a positive effect on housing prices, whilst the mortgage rate affects housing prices negatively. The mortgage rate is itself a function of the yield on 30-year Treasuries, which has a positive effect on the mortgage rate.

According to Kuttner (2012), there are two main theoretical views on how monetary policy should respond to fluctuating asset prices: the Bernanke-Gertler (1999) view that monetary policy should respond to the macroeconomic effects of asset-price movements rather than to asset prices themselves, and the view that monetary policy is itself a primary cause of asset-price cycles and should be calibrated to moderate them. The latter view is supported by Taylor (2007, 2009) and urges extreme caution in the design and implementation of monetary policy, especially as a countercyclical tool that could have unwanted effects on asset markets and the financial system, as some scholars believe happened in 2004-2008.

Building on the two theoretical approaches above, this paper would like to propose a long-run relationship between housing prices and monetary policy. This is expressed by the nationwide Case-Shiller index as a function of the average quarterly Effective Federal Funds Rate (targeted by the Federal Reserve), whilst taking into account a number of other explanatory variables such as the year-on-year quarterly GDP growth, average quarterly unemployment levels, the average quarterly Consumer Sentiment Index (as compiled by the University of Michigan), and the Total Quarterly Dwellings and Residential Building Starts Index for the United States. The long-run value of the Case-Shiller index will be modelled as:

  1. Y=YR, GDP, UNEMP, CSENT, RESB              +                           +             

The empirical analysis will attempt to capture long-run relationships in a ‘theory-light’ way, as proposed by Sims (1980), which seeks to impose as few structural restrictions on the model as possible. Moreover, it will seek to bridge the gap between a policy variable such as the Effective Federal Funds Rate and a nationwide price index of a very complex market that is being treated homogeneously (following Arestis and Karakitsos, 2007). The inclusion of classic macroeconomic indicators as explanatory variables may or may not be helpful in explaining this long-run relationship. The empirical analysis stemming from this theoretical construction should help the paper avoid the “incredible identification restrictions” imposed by structural models, as described by Sims (1980).

The paper will empirically test three main hypotheses. The first is that the monetary policy of the Federal Reserve has been strongly correlated with the cyclical movements of housing prices on the national aggregate level in the US, as captured by the S&P Case-Shiller CoreLogic Home Prices Index in the period 1950-2016. Secondly, interest rates have a cyclical pattern of acting on housing markets, with their influence peaking a certain time after they are changed and then receding. Finally, the response lag at which house prices respond most vigorously to interest rate shocks has been changing over the decades between 1950 and 2016 and could be inferred from the underlying data.

The use of the selected explanatory variables is designed to capture the supply- and the demand-side dynamics of housing markets, as interest rates are likely to affect both sides. The effects of changes in the EFFR on the supply side are captured by the movements of the Residential Buildings Starts index with a suitable lag that allows for developers to adjust to new credit conditions. GDP growth, the Consumer Sentiment Index, and unemployment levels reflect macroeconomic conditions and proxy demand for housing in the economy.

The reason for selecting the period of 1950 to 2016 is that this period is the longest that is useful for informing current monetary policy debates. This is the period in which the Federal Reserve has existed with its present legal powers and independence. The Federal Reserve was founded as a decentralised central bank in 1913 in response to several financial panics in the early twentieth century. In the first half of the century, the Federal Reserve faced three major monetary abnormalities – the First World War, the Great Depression, and the Second World War – and was forced to respond to those emergencies with measures that were not independent of fiscal policy. In 1951, the Treasury Accord was signed, which restored the independence of the Federal Reserve by eliminating its obligation to monetise the debt of the Treasury at a fixed rate. This became essential to the independence of central banking in the US and to how monetary policy is pursued by the Federal Reserve today.

  1. Existing literature

The cyclical behaviour and interdependence of monetary policy and housing markets have been researched and documented extensively.  Claessens et al (2011) show that housing markets exhibit strong pro-cyclicality, suggesting that they peak at points of the business cycle when rates are low and GDP is growing fast, in their analysis of 21 advanced economies over the period 1960-2007. Moreover, financial and housing cycles are highly synchronised across countries, as evidenced by Hirata et al (2013). Ahearne et al (2005), in a study of 18 major industrial countries between 1970 and 2005, discover that housing booms tend to be preceded by periods of monetary easing, with a lag of between one and three years. They note that central bankers have generally been unwilling to intervene to stem rises in asset prices and have followed the Bernanke-Gertler (1999) consensus regarding the relationship between monetary policy and asset markets.  However, their empirical analysis does not purport to trace a causal connection between housing-price movements and interest rates and is merely suggestive of a more complex relationship. Campbell et al (2009) sought to decompose house price movements for 23 metropolitan areas in the United States into contributing factors pertaining to real interest rates, rents, and risk premia. The authors found that risk premia are the main contributor to housing-price fluctuations in their model, and changing interest rates has negligible effects on housing-price dynamics. Dokko et al (2009) provided empirical backing for Taylor’s (2007) assertion that over-expansionary monetary policy caused the subprime housing boom by using VAR forecasting to predict hypothetical house prices under different monetary policy regimes. They found little evidence that overshooting the ‘Taylor rule’ (the point beyond which further monetary easing will cause the creation of a bubble) could satisfactorily explain the subprime boom and advocated a Bernanke-Gertler approach to monetary policy coupled with macro-prudential regulation of mortgage lending. Reinhardt and Reinhardt (2011), analysing historical housing prices and real interest rates, conclude that a ‘moderately’ different monetary policy strategy could not have prevented the housing bubble, and advocate the view that monetary policy has no long-term effects on real interest rates or housing prices, placing the emphasis on global capital flows as the main driver behind credit conditions and housing prices. Kuttner (2012) summarises the empirical findings of four studies on the dynamic effects of a 25-basis-point monetary policy shock on housing prices as displayed in Table (1):


Table 1

Immediate effect After 10 quarters Long-term
Del Negro and Otrok (2007), US, 1986-2005 0.9% 0.2% ≈0
Goodhart and Hoffman (2008), 17 OECD countries, 1985-2006 0 0.4% 0.8%
Jarocinski & Smets (2008), US, 1995-2007 0 0.5% ≈0
Sa et al (2011), 18 OECD countries, 1984-2006 -0.1% 0.3% 0.1%

Analysing the existing literature on the subject should also consider the ideological pendulum swings of twentieth-century economic thought, which is key to properly understanding adopted policy tools and the zeitgeist of economic literature on the topic. The periodical division this paper proposes consists of three main periods, which delineate the shifting paradigm on the role of housing markets in the US macroeconomy and their responsiveness to monetary policy interventions. The first period is the classic, Keynesian understanding of the subject, characteristic of the literature in the years 1950-1970.  Examples include Klaman (1956) and Naylor (1967). The second period is the era of the ‘Great Moderation’ of the years 1980-2007, heavily influenced by the decline of orthodox Keynesian thinking and the rise of monetarism and deregulation as topics of intense research interest.  The ‘manifesto’ of this new wave of economic thinking is Friedman and Schwartz’s A Monetary History of the United States (1963), which treats financial malfunctions, most importantly the Great Depression, as failures of monetary policy rather than as market imperfections to be corrected through regulation.  This school of economic thought and its relationship to the problem of monetary policy’s effects on housing markets are discussed at length by Goodhart and Hoffman (2000), Bernanke and Gertler (1999), and Cecchetti et al (2000). The transition between this period in the development of economic thought on the subject and the emerging post-crisis consensus (2008-2016) began after the bursting of the subprime bubble in 2006. Some early attempts at explanation and reconciliation between the theory and practice of monetary policy conduct and asset market instability were proposed by Mishkin (2007) and Taylor (2007) at the Jackson Hole Economic Policy Symposium in 2007, as well as by Del Negro and Otrok (2007).  As the financial crisis ran its course and the Federal Reserve reset its cycle of monetary tightening to support the flailing American economy, a new synthesis of the monetarist deregulatory understanding and the older, regulation-minded, Keynesian thinking developed. Some representatives of this emerging approach are Glaeser, Gottlieb, and Gyourko (2010), Allen and Rogoff (2011), and Williams (2015). This paper adheres to the consensual approach proposed by these authors, and whilst espousing the importance of monetary policy in regulating cyclical movements in housing markets, it also recommends the active implementation of regulatory mechanisms to achieve a more ‘peaceful’ coexistence of monetary policy interventions and stability in housing markets.


  1. Empirical discussion

1. Empirical process and 1950-2016 VECM model

The modelling process commenced with a linear regression equation of only rates and housing prices of the type:

  1. Yt= α+ βRt-n+ ϵ,

where Yt is the value of the Case-Shiller in quarter tR is the interest rate in period t-n, and

ϵis an error term. Performing an ordinary-least-squares regression on this equation produced a low Adjusted-Rof only 0.152, which suggested that the inclusion of the other business-cycle variables might be able to improve the model’s fit. Including the variables (average quarterly unemployment, quarterly GDP growth, consumer sentiment, and the residential building starts index) in the regression equation as follows:


Yt= α+ βRt-n+ γGDPt+ δUNEMPt+εCSENTt+θRESBt-4+ ϵ

raised Adjusted-R2 to 0.31, but unemployment and consumer sentiment were insignificant. Ordinary-least-squares analysis suggested that interest rates and GDP growth are negatively correlated with the Case-Shiller index, while the Residential Building Starts index is positively correlated with the Case-Shiller index when lagged four quarters. This matched the average reported time to complete a house in the US, estimated at one year (Shiller, 2008). Interest rates are more strongly correlated with the C-S index when lagged, suggesting the peak response of house prices to interest rates shocks occurs with a delay. Removing the insignificant explanatory variables and reducing the regression equation to the form


Yt= α+ βRt-n+ γGDPt+θRESBt-4+ ϵ

produced results (by the ordinary-least-squares method) which suggested that all three remaining variables were significant at the 99% confidence level, with Adjusted-Rof 0.318.

All variables in the model (the housing-price index, interest rates, GDP growth, residential building starts index) are time series and are likely to exhibit non-stationarity, which would bias ordinary-least-squares model estimates as it makes the method prone to reporting spurious regression (Granger and Newbold, 1974). Indeed, the reported Adjusted-R2 of 0.318 seems to be unrealistically large for such a complex, long-run, multivariate econometric relationship. This proposition was confirmed by the Augmented Dickey-Fuller test, which confirmed the existence of a unit root for three of the time series (the EFFR, the Case-Shiller index, and the Residential Building Starts index) and demonstrated that these three time series are non-stationary in its levels I(0), but are stationary in their differences I(1), at the 95% confidence interval, with four lagged differences to account for any serial correlation of the error term in the Dickey-Fuller regression. The year-on-year Quarterly GDP Growth series is I(0) stationary under the same conditions.

In the case of non-stationary time series, applying an unrestricted vector autoregression (VAR) model (Sims, 1980) is not advisable as it is likely to bias the estimated regression coefficients by spuriously rendering them higher (Brooks, 2014 & Gujarati, 2015). In the proposed model, performing a restricted VAR with first-differenced time series is also not advisable, as this is likely to disregard crucial information about the long-term relationships between the variables (Gujarati, 2015). The Johansen test (Johansen, 1988) suggests that long-run cointegrating relationships exist between the four variables of the model.

Therefore, the empirical process proceeded with a vector autoregression model with error correction (made possible by the cointegration relationships demonstrated by the Johansen test), producing a vector error correction model (VECM). The overall VECM model for the period 1950-2016 consists of the following four autoregressive equations (one for each of the four variables of the model) with 10 lags each, whose details are presented in the table below:

Figure 1

The VEC autoregressive equation for the housing-price index contains 38 parameters and has R2 of 0.7912. The overall model’s Akaike information criterion is 7.45. Additionally, four other VECM models were used for the periods 1950-1970 (10 lags), 1970-1990 (10 lags), 1990-2006 (10 lags), and 2006-2016 (four lags due to the shorter duration of the period), as the Johansen test showed proof for cointegration between the series for all four periods, and the Augmented Dickey-Fuller test showed that all time series were non-stationary in their levels but stationary in their differences within the sub-periods. The orthogonalised impulse-response function of an interest-rate shock on housing prices (which takes account of the fact that the error terms of each of the four autoregressive equations are correlated) for the overall period between 1950 and 2016 is represented in Figure (2).

Figure 2

The impulse-response function demonstrates that on average for the nearly 60-year period, a unit shock (100bp) in the Effective Federal Funds Rate (an interest rate shock) will propagate through the economy to cause a decrease in the value of Case-Shiller index, peaking at around 14 quarters after the shock (at approximately -0.9%) and then receding. This result is in line with the estimates of Kuttner (2012), whose impulse-response function analysis obtained through a structural VAR model with error correction suggests a peak in the response of housing prices (-0.35%) to an interest-rate shock of 10 basis points at 12 quarters.  The VECM model appears to operate as expected with regards to the other explanatory variables, yielding the following impulse-response function for the effects of an interest rate shock on quarterly GDP growth, shown on Figure (3).

Figure 3


The VECM model also yields a theoretically-expected response of the residential building starts index to an interest-rate shock, as captured by the impulse-response function graph on Figure (4):

Figure 4

The above response function suggests an empirical conclusion in line with the discussion of Arestis and Karakitsos (2004) that residential building (but not supply of ready housing) responds quickly to housing-price shocks. In the medium- to long-run, as the market gets saturated with new buildings, increased housing prices could exert a negative pressure on residential building activity, as shown in Figure (5), which captures the response of the Residential Building Starts Index to a housing-price shock:

Figure 5

2. Sub-period VECM models

The empirical analysis proceeded with breaking down the initial period (1950-2016) into four sub-periods (1950-1970, 1970-1990, 1990-2006, and 2006-2016), to capture the changing dynamics of the monetary policy transmission mechanism throughout the main period, as reflected by the changing lag in the peak response of housing prices to an interest-rate shock. The following four graphs [Figures (6), (7), (8), and (9)] show the impulse-response functions for each of the above periods and demonstrate that the lag after which housing prices reach peak response to an interest-rate shock has been changing over the four sub-periods:

Figure 6

Figure 7

Figure 8

Figure 9

The three VECM models for 1950-70, 1970-90, 1990-2006 and the impulse-response functions of housing prices to an interest rate shock shown above [Figures (6), (7), (8), and (9)], suggest
that the time lag after which housing prices respond most strongly to interest-rate shocks
has been decreasing since 1950: from 23 quarters for 1950-1970, to 15 quarters for 1970-
1990, to 13 quarters for 1990-2006. Possible reasons for this could be the
increased levels of mortgage debt in the economy, the higher velocity of money since 1950, and ‘easier’ mortgages due to government policy stimulating home ownership, including the purchase of mortgage securities on the secondary mortgage market by government-backed enterprises. However, the tendency for the lag to decrease breaks down for the period 2006-2016, where the VECM analysis suggests the peak response to an interest rate shock arrives at around 17 quarters after the shock.

3. Empirical conclusions

The following table summarises the results obtained with the four models and the overall model:

Table 2: Effects of a 100bp increase in the EFFR on the Case-Shiller Index

Lag to reach peak response Magnitude of effects
Immediate (1-4 quarters) After 15 quarters Long-run
Overall model 


14 +0.25% -0.8% -0.3%
1950-70 23 +0.4% -0.5% ≈0
1970-90 15 +0.2% -0.9% -0.5%
1990-06 13 -0.5% -2.3% ≈0
2006-2016 17 -1% -2.8% -2.5%

The empirical results summarised in the table clearly demonstrate that monetary policy has been acting with a decreasing lag and increasing magnitude throughout the decades between 1950 and 2016. In the first period (1950-2016), monetary policy seems to have had a smaller effect with a greater lag on housing markets than it did in the third period (1990-2006).

The reversal of the decrease in the lag could be a side effect of the decreased velocity of money and mortgage indebtedness after 2007-2008. Figure (10) shows the change in the average lag in the peak response of the Case-Shiller Index to a positive shock in the EFFR (the grey curve) for the periods 1950-1970, 1970-1990, 1990-2006, and 2006-2016, plotted against the changing velocity of money in the American economy (the red curve) and its trend line (the punctured blue curve).

Figure 10

The decreasing lag in the response of housing prices to monetary policy shocks could also be ascribed to the increased mortgage indebtedness in the American economy, which makes housing markets more dependent on credit to support demand and less dependent on already accumulated savings or other non-mortgage means of financing housing purchases. Figure (11) plots the curve of the changing lag (in grey) against the levels of outstanding mortgage debt in the US economy (in red), as captured by the ratio of outstanding mortgage debt to nominal GDP for each quarter of the period:

Figure 11

  1.        Policy implications

1. Monetary policy and asset-market regulation

Monetary policy is a blunt and imperfect tool of moderating housing markets because of the impulse-response lag and the changing magnitude and amplitude of the response function. Housing-market price responses are mainly a ‘side effect’ of the macroeconomic stabilisation medicine, as the Federal Reserve targets consumer-price inflation (CPI before 2000, the chain-type Price Index for Personal Consumption Expenditures after 2000). As housing markets have proven to be systemically important (the 2007 subprime crisis was ‘contagious’ to the wider financial system not only in the US but worldwide), monetary and regulatory authorities need additional tools to moderate housing-price fluctuations, which specifically target credit conditions in housing markets. The large costs imposed by the subprime crisis demonstrated the critical role of housing markets in the American financial system and their complex interplay with bank balance sheets through the vehicle of financial derivatives, which were mostly developed after the 1980s, as deregulation took hold as policy philosophy and financial modus vivendi.

2. The subprime crisis from a policy perspective


The sustained growth in housing prices between the years 2000 and 2006 was cause for much “irrational exuberance” among homeowners and financiers. In a San Francisco study from 2005, one year before the peak of the bubble, Shiller (2008) discovered that the average price growth expected by homebuyers in the ten-year period after the purchase of their home was 14% annually. Subprime mortgages, a financial innovation which allowed would-be homebuyers with low credit ratings to access mortgage lending, flourished. According to Shiller (2008), subprime lending was hailed as an example of a market-based ‘social equaliser’, capable of providing low-income households with access to property ownership. In the financial theory underlying subprime lending prior to the crisis, it was expected that risk should be minimised by the variable interest rates on subprime loans and through the ‘repackaging’ of mortgages into complex derivative products. It was also held that even if subprime lenders defaulted on their loans, which they were likelier to do on average than ‘prime’ lenders, the foreclosure of their property would be sufficient to recover the loan as prices would have risen in the meantime. Buckley (2011) cites figures from a Federal Reserve report to show that subprime lending rose by 25% annually over that period 2001-2007, by far the fastest-growing mortgage product in this period. Mian and Sufi (2009) demonstrate that the expansion of mortgage credit in predominantly subprime-borrower areas in the US happened despite declining real incomes over the period. This expansion was negatively correlated with loan denial rates, which fell to record lows between 2002 and 2005. High demand for mortgages functioned as a self-fulfilling prophecy, driving up real estate prices. This process led to the relaxation of lending requirements by mortgage lenders, as the expectation of a sustained price rise was shared by buyers, lenders, and builders. Lybeck (2008) suggests that demand for subprime mortgages was strengthened by a ‘lemon-market’ situation of asymmetric information, as lenders did not properly disclose the full conditions of subprime loans, leading to widespread misunderstanding of the true long-term mortgage costs past the initial ‘honeymoon’ period of low rates. Further proof of how the ‘perpetuum mobile’ of the feedback loop between lenders and borrowers contributed to the relaxation of credit requirements is provided by Dell’Ariccia, Igan and Laeven (2008) in their analysis of mortgage lending activity by postcode. Areas which experienced the greatest rise in mortgage demand were also less likely to have failed loan applications. Furthermore, lenders were likely to accept lower loan-to-income ratios in these areas than in others where mortgage activity was less intense.

The end the dot-com bubble in 2001 prompted the outflow of financial capital from the stock market and into the property market, which was regarded as a safe bet with sustainable upside potential. Additionally, the low-interest environment incentivised investors to explore higher-return but riskier sectors of the mortgage market such as subprime lending. Mortgage-backed securities (MBS) were thus perceived as relatively safe, not only because of the ‘real’ collateral that property represented but also since they pooled thousands of mortgages originated in various parts of the US. Nonetheless, geographical diversification proved insufficient to arrest the fall in value of MBS when the housing market collapsed nationally. MBS were combined into collateralized debt obligations (CDOs) and sold to other investors. This technique was used to additionally reduce risk but eventually contributed to contagion in the global financial system in 2007-8. Rating agencies, which were supposed to provide an objective assessment of the viability of MBS and CDOs, consistently rated mortgage securities highly. A possible conflict of interest in the rating process is suggested by Shiller (2008), as rating fees were paid by the same financial institutions that originated the securities. The oligopolistic dominance of the rating market by only three rating agencies in the US (Fitch, Moody’s, and Standard and Poor’s) created the risk of collusion in rating certain instruments in certain ways.

The drive towards the ‘ownership society’ promoted by the Clinton and Bush administrations, whilst not limited to housing, gave an ideological boost to essentially fiscal policies seeking higher national levels of home-ownership. These policies included the support of subprime lending through taxpayer-backed collateralisation of subprime mortgages performed through the purchase of mortgage-backed securities (MBS) on the secondary-mortgage market by government-sponsored enterprises (GSEs) like the Federal National Mortgage Association (FNMA) and the Federal Home Loan Mortgage Corporation (FHLMC).  From 1995 onwards, FNMA and FHLMC were stimulated to purchase MBS through tax incentives. In 1996, the two GSEs were mandated to have at least 42% of purchased MBS underwritten by mortgages extended to low-income borrowers. By 2008, the two GSEs owned $5.1 trillion in residential mortgages, which constituted about half of the US mortgage market. According to Davies, a sizeable proportion of those were subprime loans: by the second quarter of 2008, FNMA had purchased mortgage derivatives equivalent to $553 billion of extended mortgage loans. After the housing bubble burst, a substantial proportion of these products were devalued, which caused severe balance-sheet problems for the two GSEs and their refinancing by the Treasury.

Legislative initiatives designed to further home-ownership also contributed to the subprime bubble. According to Davies, the 1977 Community Reinvestment Act set the stage for the ascent of subprime lending two decades later. The Act mandates lenders to serve low-income borrowers in their area and encourages them to consider a range of socioeconomic criteria which had little to do with creditworthiness criteria. The Act requires “regulated financial institutions […] to help meet the credit needs of the local communities in which they are chartered.” This legal framework created incentives for mortgage lenders to aggressively seek subprime borrowers and extend predatory lending under the pretext of complying with legal obligations.

Thus, when the Federal Reserve embarked upon a monetary tightening cycle in 2004, raising the EFFR from 1.00% in May 2004 to 5.25% in July 2006, it introduced a shock to a financial system already plagued by acute imbalances, unsustainable leverage levels, and high interconnectedness between housing markets and banking. The decision to tighten monetary policy was motivated by the traditional criteria of accelerating inflation (3.1% in May 2004, according to the FRED database) and a tight labour market (5.6% unemployment in the second quarter of 2004, FRED), which followed an extended, three-year period of low EFFR at 1.00%. Consumer-price inflation targeting could not have revealed the true consequences of tighter credit for housing markets, and the Federal Reserve acted in good faith and according to the prescriptions of its legal mandate when it increased rates between 2004 and 2006. The outcome demonstrated that without efficient regulation, the goals of macroeconomic stability and asset-price stability could not be achieved at the same time, and certainly not in the highly-deregulated, financially-innovative environment of the 2000s.

3. Towards a combined monetary-macroprudential approach


Should monetary policy target asset prices? Under the Bernanke-Gertler (1999) approach, it should only do so if asset prices fulfil two conditions: firstly, that they have deviated from valuation fundamentals (the classic symptom of a bubble), and secondly, that asset price dynamics are having or are likely to have substantial effects on macroeconomic and financial stability. Dokko et al’s (2010) interpretation is that the Federal Reserve should not target asset prices per se: since they are likely to not correspond to underlying financial fundamentals, they are unlikely to be good informers of macroeconomic indicators the Federal Reserve targets: inflation and unemployment. They quote Assemacher-Wesche and Gerlach (2009), who advise caution in the use of monetary policy to directly influence asset prices, as this might have a disproportionate and unwanted effect on output and inflation. This conclusion is based on their empirical result that the effect of monetary policy on housing prices is only about three times greater than its impact on GDP, a multiple not large enough to warrant raising rates to deflate a bubble but usher in a recession.

These conclusions suggest another approach to asset and housing-price regulation, which is employing financial and macroprudential regulation tools in conjunction with monetary policy. This approach is not only theoretically sound but also warranted by the experience of deregulation that brought the subprime crisis when it was combined with tighter monetary policy (discussed in detail in Part VI, Chapter 2).  Dokko et al (2010) find little evidence that monetary policy accounted for a “substantial share” of the rise in US housing prices between 2003 and 2006, the height of the subprime bubble, but they note that the changed housing-finance environment of that period was a large contributor. Kohn (2008) outlines several lodestar principles for central bankers if they are to respond to asset price fluctuations to an extent that is greater than the immediate influence of asset prices on price stability and employment. The first principle is that of detection: whether the bubble could be ‘diagnosed’ as it is happening, or whether it could only be recognised after it has burst. The second recognises the inherent compromise in using monetary policy to regulate asset markets and urges policy-makers to ask themselves whether their actions will compromise other desirable macroeconomic outcomes. The third question is whether a monetary policy intervention will be effective in moderating the bubble; and the fourth is whether other policy response would be more effective.

This paper answers those four questions in the following way. The subprime bubble could and was diagnosed as it was happening, but the bursting point could not and was not. Monetary policy actions were taken by the Federal Reserve in 2004 in response to accelerating inflation and tightening job markets in the US, and not to the housing boom. This tightening cycle was the straw that broke the financial house of cards built through the securitisation and repeated re-selling of mortgage-backed securities. Thus, it could be said that monetary policy actions are ‘effective’ in addressing a bubble, but always with a lag and very often with unintended and far-reaching consequences. The answer to the fourth question is therefore that there are other policy responses which could and would have been more effective in addressing the bubble, if they are not employed at a point beyond which there is a high probability of deflating the bubble with recessionary side effects. The subprime bubble was so economically-detrimental because of its derivative-driven entanglement with banking. Dokko et al (2010) note that financial crises are usually preceded by bubbles incited by deregulation or financial innovation and that these crises usually involve disruption in credit supply created by excessive leverage in the system, which suddenly becomes unsustainable.

Therefore, financial regulation tools should be considered to reduce the risk of systemically-dangerous housing-market bubbles and discourage speculative lending. One of the strongest candidates for such a tool is macroprudential regulation, which is gathering scholarly attention and policy-maker support in the United States, Europe, and China. Macroprudential regulation involves setting countercyclical capital adequacy and buffer requirements for lenders, which may adjust as the business cycle evolves. Daniel K. Tarullo, Governor of the Federal Reserve, described the post-crisis approach to regulation pursued by the Federal Reserve as “a macroprudential reorientation of our bank regulatory policies” that “will require a range of continuing work on resiliency, on other structural measures, and on the effective blending of macroprudential with traditional microprudential regulatory and supervisory policies” (2013). Under a flexible macro-prudential regulation framework, capital adequacy requirements or loan-to-value ratios will tighten to discourage financiers from speculative lending at high points in the housing cycle, and will relax to help the market recover during low points. Dokko et al (2010) note that macroprudential regulation may be especially well-suited to housing markets in the US, as it could break the “feedback loop” leading to unsustainable leverage levels for households and lenders. The illusion of diffusing risk through securitisation and the expectation that housing prices will continue rising caused the relaxation of lending standards of mortgage credit, already spurred by deregulation. Had macroprudential measures been in place at the time, they could have prevented excessive relaxation by setting tighter leverage requirements for both mortgage lenders and mortgage borrowers.

A combined, cyclically-adjusted approach to regulation will add precision to monetary policy interventions in their housing-market dimension by changing the conditions under which mortgage-lending financial institutions could extend financing to homebuyers. For example, at moments of the housing-price cycle in which prices are on the rise and there is a lot of transactional activity, tighter lending conditions could be imposed on mortgage-lending institutions to counterbalance the liquidity stimulus of loose monetary policy. Such tighter conditions could include higher mandated loan-to-value ratios (LTV) or a cap on debt-to-income ratios for new mortgage takers (they must cover a greater portion of the value of the property they are about to purchase with their own funds). A characteristic of subprime lending before the bursting of the bubble in 2006 was very high LTV ratios, in some cases exceeding 100%. This meant that the mortgage taker did not need to contribute any savings towards financing their purchase and could completely rely on their loan to cover all associated costs. Another cyclical prudential intervention could be made at the level of the bank balance-sheet by imposing higher capital buffer requirements for mortgage lenders at high points in the housing-market cycle, once again to counteract the incentive to extend cheaper and riskier loans associated with loose monetary policy. Such intervention might, for example, increase the ratio of extended mortgage loans (assets on the bank balance-sheet) covered by a capital buffer from 10% at low points in the cycle to 20% at high points in the cycle. This prudential tightening could reasonably be expected to gradually cool off property markets despite loose monetary policy, especially in a financially-advanced economy where most property purchases are enabled with a mortgage loan. In the scenario where inflation is rising and the Federal Reserve feels the need to tighten monetary policy to control CPI, capital-buffer and LTV requirements for mortgage lenders could be relaxed to prevent a ‘crash-landing’ for property markets, as happened in 2006 after the Federal Reserve had been tightening monetary policy for the previous two years. Some mandatory components of a soundly-designed system of macro-prudential regulation are the official identification of systemically-important financial intermediaries, to be subjected to a special regime of regulation, prudential requirements, and restructuring if they run into trouble; the creation of transparent methods of determining capital requirements which take full account of each institution’s contribution to systemic risk; and the imposition of flexibility in the design of capital requirements, so that they could be deployed counter-cyclically to deflate a bubble but also pro-cyclically to support a flailing economy.

Some of the potential shortcomings of monetary policy as an asset-price regulation tool are shared by macroprudential regulation. The difficulty in identifying a developing bubble and thus knowing how and when to modify prudential requirements to achieve the necessary result is problematic. Macroprudential regulation also involves a fundamental trade-off between short-term growth and long-term stability. Whilst it will almost surely inhibit growth through tightening credit conditions, the additional stability it would bring should compensate for this in the longer run and lead to better long-run growth performance. Popov and Smets (2012) recommend a balancing act under which macroprudential tools are more actively employed during ‘bad’ booms driven by debt finance but less so during ‘good’ booms driven by equity finance, thus addressing the market failures and externalities which contribute to ‘over-borrowing’ but not stifling the positive contribution of financial markets to growth.  Once again, however, the problem of identification of a ‘good’ versus ‘bad’ boom remains, and the macro-symptoms of debt versus equity financing proposed by Popov and Smets are not readily obvious.

Macroprudential measures also need to be coordinated with the wider legal framework within which housing markets operate, such as the 1977 Community Reinvestment Act and the legal rules stipulating the role of FNMA and FHLMC. Important legislative decisions such as the repeal of the Glass-Stegall Act in 1999, the Sarbanes-Oxley Act, and the Dodd-Frank Act, as well as the international prudential framework of Basel III, should serve as the wider regulatory environment within which housing-market-specific macro-prudential policy measures are designed. The impact and success of legislative instruments containing substantial macroprudential provisions, such as the Dodd-Frank Act in the United States and Directive 2013/36/EU, remain to be quantified and discussed by scholars and policy-makers.

Whilst theoretically sound, macroprudential approaches have been implemented in practice only sparsely, and much additional research is needed into macro-prudential instruments which specifically target housing markets. In the aftermath of the crisis, some regulators, such as the Reserve Bank of New Zealand, have specifically targeted property markets, setting higher loan-to-value ratios for mortgage lenders to moderate the post-crisis property boom in the country.  Since their introduction in October 2013, macro-prudential solutions in New Zealand appear to have contributed to cooling down domestic property markets (Hargreaves, 2016). However, due to the relative novelty of macro-prudential solutions, a conclusive assessment
is still not possible, and further research is needed to confirm them as a useful tool in moderating housing-market cycle. This is especially necessary in the US due do the following
characteristics of its economy: high levels of mortgage debt and a strongly developed secondary mortgage market dependent on government-sponsored enterprises, the high
systemic importance of housing markets within the US financial architecture, and the high significance of housing owner-occupation in the political economy of the United States. Nonetheless, a combined approach to housing markets which integrates prudential
requirements for mortgage lenders and borrowers with monetary policy instruments holds the promise of more moderate and less harmful future cyclicality in housing markets.

  1. Conclusions

The subprime mortgage crisis and its detrimental effects on financial stability of not only the United States but the entire world raised pressing concerns over the role of monetary policy in housing markets and possible alternatives to it. This paper proposes a long-run model which seeks to trace the empirical connection between the monetary policy of the Federal Reserve as expressed through its targeting of the Effective Federal Funds Rate (EFFR), and the dynamics of housing prices in the United States in the period 1950-2016 whilst influenced by classic business cycle variables – quarterly year-on-year GDP growth and the residential building starts index. The empirical analysis established that there is a significant response of housing prices in the United States to interest-rate shocks, that this response peaks 15 quarters after the shock on average for the whole period, and at 14, 23, 15, 13, and 17 quarters for the sub-periods 1950-70, 1970-90, 1990-2006, and 2006-06. Whilst the lag had been decreasing from 1950 until 2006, for the last period it increased again, suggesting that the crisis has changed monetary policy transmission mechanisms with respect to housing markets. The paper discusses the implications of monetary policy for housing markets before the subprime mortgage crisis and supports the view that deregulation rather than monetary policy was the underlying cause for the expansion of the bubble, and the role of the Federal Reserve in the bursting of the bubble could be described as adding the last straw, and not as setting the stage. The paper thus proposes a combined approach to the regulation of housing markets, which complements the effects of monetary policy on housing markets through macroprudential regulation of mortgage lenders and borrowers. This new regime holds the promise of moderating the duration and amplitude of housing cycles and of removing the detrimental interdependencies between them, the wider financial system, and the macroeconomy. Nonetheless, further research is necessary to confirm the usefulness of macroprudential measures targeting the mortgage market and to ensure that the benefit of long-term stability outweighs the loss in short-term growth.

WORD COUNT:  8150 (excluding bibliography, graphs and tables)

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VIII. APPENDIX A – Vector Error Correction Model Specifications

Vector Error Correction Estimates, VECM Model – 1950-2016
 Sample (adjusted): 11 239
 Included observations: 229 after adjustments
 Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
CSI(-1)  1.000000
RATES(-1)  1.226742
[ 1.20334]
GDP(-1)  12.22206
[ 4.89321]
RBI(-1)  20.44331
[ 2.20252]
C -217.7614
Error Correction: D(CSI) D(RATES) D(GDP) D(RBI)
CointEq1  0.001172 -0.005408 -0.011957 -0.002895
 (0.00492)  (0.00352)  (0.00406)  (0.00089)
[ 0.23823] [-1.53794] [-2.94181] [-3.26414]
D(CSI(-1))  0.569466 -0.025939 -0.074337  0.010610
 (0.07259)  (0.05188)  (0.05996)  (0.01309)
[ 7.84445] [-0.50001] [-1.23968] [ 0.81087]
D(CSI(-2)) -0.071266  0.120897 -0.021579  0.017371
 (0.08083)  (0.05776)  (0.06676)  (0.01457)
[-0.88172] [ 2.09311] [-0.32321] [ 1.19235]
D(CSI(-3))  0.474439 -0.030045  0.050034  0.017780
 (0.08183)  (0.05848)  (0.06759)  (0.01475)
[ 5.79797] [-0.51381] [ 0.74023] [ 1.20548]
D(CSI(-4))  0.212019  0.001916 -0.041491  0.002038
 (0.08669)  (0.06195)  (0.07161)  (0.01563)
[ 2.44576] [ 0.03092] [-0.57943] [ 0.13042]
D(CSI(-5)) -0.219198  0.039652  0.190370 -0.004689
 (0.08613)  (0.06155)  (0.07114)  (0.01552)
[-2.54510] [ 0.64427] [ 2.67593] [-0.30206]
D(CSI(-6)) -0.253657 -0.075326 -0.020692 -0.002060
 (0.08797)  (0.06287)  (0.07267)  (0.01586)
[-2.88337] [-1.19820] [-0.28475] [-0.12991]
D(CSI(-7)) -0.110334  0.048489  0.052794 -0.001506
 (0.08429)  (0.06024)  (0.06963)  (0.01519)
[-1.30894] [ 0.80497] [ 0.75824] [-0.09911]
D(CSI(-8))  0.279021  0.056786 -0.015891 -0.008576
 (0.08323)  (0.05948)  (0.06875)  (0.01500)
[ 3.35237] [ 0.95474] [-0.23114] [-0.57161]
D(CSI(-9)) -0.102423 -0.003060  0.012664  0.009107
 (0.07828)  (0.05594)  (0.06466)  (0.01411)
[-1.30838] [-0.05469] [ 0.19584] [ 0.64543]
D(RATES(-1))  0.134934  0.167213  0.214865  0.003931
 (0.10256)  (0.07329)  (0.08471)  (0.01849)
[ 1.31570] [ 2.28159] [ 2.53636] [ 0.21266]
D(RATES(-2))  0.028830 -0.192454 -0.218852 -0.023104
 (0.10501)  (0.07504)  (0.08674)  (0.01893)
[ 0.27454] [-2.56466] [-2.52309] [-1.22064]
D(RATES(-3)) -0.018785  0.211749  0.031865 -0.009162
 (0.10589)  (0.07567)  (0.08747)  (0.01909)
[-0.17740] [ 2.79833] [ 0.36431] [-0.48005]
D(RATES(-4)) -0.107292 -0.017555 -0.177345 -0.025484
 (0.10522)  (0.07519)  (0.08691)  (0.01897)
[-1.01971] [-0.23348] [-2.04049] [-1.34369]
D(RATES(-5)) -0.254781  0.129015 -0.135155  0.002881
 (0.10204)  (0.07292)  (0.08429)  (0.01839)
[-2.49686] [ 1.76929] [-1.60350] [ 0.15666]
D(RATES(-6)) -0.119972 -0.084067  0.010946 -0.014610
 (0.10468)  (0.07480)  (0.08647)  (0.01887)
[-1.14609] [-1.12381] [ 0.12659] [-0.77430]
D(RATES(-7))  0.073236 -0.187809 -0.223583  0.070203
 (0.09868)  (0.07052)  (0.08151)  (0.01779)
[ 0.74215] [-2.66324] [-2.74290] [ 3.94683]
D(RATES(-8))  0.157041  0.059198 -0.044168  0.024782
 (0.10079)  (0.07203)  (0.08326)  (0.01817)
[ 1.55805] [ 0.82187] [-0.53050] [ 1.36403]
D(RATES(-9)) -0.027943  0.002263 -0.111175  0.017306
 (0.10134)  (0.07242)  (0.08371)  (0.01827)
[-0.27574] [ 0.03125] [-1.32814] [ 0.94742]
D(GDP(-1)) -0.123129  0.078299  0.151759  0.013507
 (0.09488)  (0.06780)  (0.07837)  (0.01710)
[-1.29771] [ 1.15480] [ 1.93634] [ 0.78979]
D(GDP(-2)) -0.015466 -0.044566  0.048189  0.026754
 (0.08744)  (0.06248)  (0.07223)  (0.01576)
[-0.17688] [-0.71324] [ 0.66720] [ 1.69755]
D(GDP(-3)) -0.157824  0.087698  0.084676  0.058859
 (0.08689)  (0.06209)  (0.07177)  (0.01566)
[-1.81643] [ 1.41243] [ 1.17981] [ 3.75821]
D(GDP(-4))  0.092462  0.005824 -0.488455  0.028994
 (0.08869)  (0.06338)  (0.07326)  (0.01599)
[ 1.04255] [ 0.09189] [-6.66760] [ 1.81371]
D(GDP(-5))  0.041238  0.137058 -0.005387  0.053225
 (0.09466)  (0.06765)  (0.07820)  (0.01706)
[ 0.43562] [ 2.02603] [-0.06889] [ 3.11926]
D(GDP(-6))  0.034562 -0.091546  0.039998  0.008071
 (0.07552)  (0.05397)  (0.06238)  (0.01361)
[ 0.45764] [-1.69625] [ 0.64116] [ 0.59289]
D(GDP(-7))  0.029908  0.093364  0.093863  0.010641
 (0.07518)  (0.05373)  (0.06210)  (0.01355)
[ 0.39780] [ 1.73772] [ 1.51137] [ 0.78522]
D(GDP(-8))  0.007454 -0.029384 -0.262385 -0.005983
 (0.07434)  (0.05312)  (0.06141)  (0.01340)
[ 0.10026] [-0.55311] [-4.27287] [-0.44653]
D(GDP(-9))  0.029454  0.027278  0.072170  0.029410
 (0.07323)  (0.05233)  (0.06049)  (0.01320)
[ 0.40220] [ 0.52124] [ 1.19306] [ 2.22797]
D(RBI(-1))  0.399749 -0.391517 -0.355501  0.135109
 (0.38960)  (0.27841)  (0.32182)  (0.07022)
[ 1.02606] [-1.40626] [-1.10467] [ 1.92396]
D(RBI(-2))  0.391419 -0.650688 -0.519488  0.094697
 (0.38895)  (0.27795)  (0.32128)  (0.07011)
[ 1.00634] [-2.34103] [-1.61691] [ 1.35072]
D(RBI(-3))  0.908605 -1.612046 -0.429695 -0.140223
 (0.38846)  (0.27760)  (0.32088)  (0.07002)
[ 2.33900] [-5.80716] [-1.33913] [-2.00263]
D(RBI(-4))  0.225176 -0.387330  1.177709  0.002829
 (0.43206)  (0.30875)  (0.35689)  (0.07788)
[ 0.52117] [-1.25449] [ 3.29992] [ 0.03633]
D(RBI(-5)) -0.175066  0.916558  1.472195  0.004159
 (0.44700)  (0.31943)  (0.36923)  (0.08057)
[-0.39164] [ 2.86932] [ 3.98715] [ 0.05162]
D(RBI(-6))  0.368221  0.710703  0.702079 -0.152289
 (0.46798)  (0.33443)  (0.38656)  (0.08435)
[ 0.78683] [ 2.12515] [ 1.81620] [-1.80536]
D(RBI(-7)) -0.091720  0.280627  0.364651 -0.048992
 (0.47455)  (0.33912)  (0.39199)  (0.08554)
[-0.19328] [ 0.82751] [ 0.93025] [-0.57275]
D(RBI(-8)) -0.120155  0.138050 -0.411256 -0.117370
 (0.47280)  (0.33787)  (0.39054)  (0.08522)
[-0.25414] [ 0.40859] [-1.05304] [-1.37724]
D(RBI(-9))  0.398687 -0.375771  0.245456 -0.052705
 (0.47299)  (0.33800)  (0.39070)  (0.08526)
[ 0.84291] [-1.11174] [ 0.62825] [-0.61820]
C  0.029020 -0.021871 -0.014823 -0.006001
 (0.06711)  (0.04796)  (0.05544)  (0.01210)
[ 0.43241] [-0.45603] [-0.26739] [-0.49607]
 R-squared  0.790062  0.487346  0.645304  0.321813
 Adj. R-squared  0.749393  0.388036  0.576593  0.190437
 Sum sq. resids  191.3956  97.73971  130.5917  6.218414
 S.E. equation  1.001035  0.715350  0.826878  0.180436
 F-statistic  19.42679  4.907331  9.391601  2.449550
 Log likelihood -304.3980 -227.4500 -260.6285  87.97380
 Akaike AIC  2.990375  2.318341  2.608109 -0.436452
 Schwarz SC  3.560163  2.888129  3.177897  0.133336
 Mean dependent  0.149241 -0.012489 -0.001747 -0.001503
 S.D. dependent  1.999644  0.914442  1.270756  0.200538
 Determinant resid covariance (dof adj.)  0.010773
 Determinant resid covariance  0.005214
 Log likelihood -697.8797
 Akaike information criterion  7.448732
 Schwarz criterion  9.796594

IX. APPENDIX B – Data Sources

The data sources used in this paper are:

1) the Federal Reserve Economic Data database (FRED) for the following time series: average quarterly Effective Federal Funds Ratesyear-on year quarterly GDP growth, and average quarterly unemployment levels

2) the University of Michigan Surveys of Consumers Database for the following time series: average quarterly Consumer Sentiment Index

3) the Robert Shiller Online Data website, part of the Yale University website, for the following time series: Case-Shiller Home Prices Index


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