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The Role of Speculation in Real Estate Cycles

By Cecilia, Ju The Role of Speculation in Real Estate Cycles

Technical Presentation: The Role of Speculation in Real Estate Cycles

Part I: Overview:

  • Outrage over Real Estate Cycles: Across countries, it is a commonly held view that real estate cycles are the product of speculation – when speculation drives up demand, prices skyrocket and vice versa. However, it is in the nature of real estate prices to fluctuate on a cyclical basis. In reality, housing prices also depend not just on speculative demand, but also heavily on supply and interest rates. The model put forth by Malpezzi and Wachter examines the impact of speculation and housing supply on price and volatility.
  • The Fundamentals of Asset Pricing: If we view housing at an asset, value is then based on the flow of services yielded over time. Thus, the value of a home is equivalent to the expectation of profit from rent over time (controlling for interest rates).
    • Market value of a unit = present value of net rents = (rental price per unit of housing services) * (the quantity of housing services produced by a unit)

  • V = market value of a unit
  • E = expectations operator
  • R = rent
  • C = maintenance costs
  • i = interest rates
    o In cases where net rents are constant over a long time horizon:


o If we account for a property that has increasing value:

• c: cap rate: the rate of return on a real estate investment property based on the expected income the property will generate.

• g: rate at which the net rent for the property is growing
• i: interest rate

So what is Speculation? The clever cheeky definition given by Malpezzi and Wachter is two-pronged: (1) If I purchase a home, it is investment but (2) If someone else buys, its speculation.

So if not a synonym for investment, then what is speculation?

o It depends on the time-horizon: rather than buying and holding, a speculation is a case of short-term ownership. Short-term ownership is defined as an ownership period in which the owner does not develop or make use of the property. Instead, the owner holds the property vacant in anticipation of a price increase and profitable sale.
• The key is to obtain optimal timing.

o Was their arbitrage involved? Arbitrage is easier to achieve in thick/liquid markets. A thick/liquid market is characterized by transparent pricing, many market participants (home buyers and sellers), as well as lots of information on prices and the market in general. On the other hand, thin/illiquid markets are characterized by price volatility and costly information. Thus, a higher amount of participants yields greater market stabilization. This is somewhat intuitive: when there are less participants in the market the official “market price” becomes less established and house prices depend more on each individual’s pricing range.
• However, it is important to note that an influx of ill-informed market participants actually contributes to destabilizing the market; these new entrants are generally more willing to overpay because they are engaging in short term investment.

o Expectations must be inaccurate: inaccurate expectations also lead to overpaying/underpaying or overpricing/underpricing for homes.

• Expectations are important because they affect:
o Real estate price
o Rent growth expectations
o Investors often speculate on a continuation of the past high rates of price appreciation. A housing bubble occurs when formulation of subjective probabilities based on the low likelihood of market collapse creates disaster myopia, in which the probability of low frequency shocks is not factored into the decision-making of market participants.

• Generators of Housing Bubbles: Backward facing/adaptive expectations driven speculative pricing behavior affects investment decisions → increases prices → increases supply (relative to demand) → unsustainable prices → bursting of the housing market → optimistic investors are wiped out as they lose capital and have no agency to continue participating in the housing market → credit crunch

o The “rational” bubble: serial correlation in price changes

where = overvaluation amount

Rational investors will be willing to invest and purchase a home overvalued by quantity b_t as long as b_t is expected to grow at a greater rate than that of interest rates. Serial correlation of price increase is necessary for the formation of housing bubbles.

The Impact of Environmental Regulation:

Excessive regulations → decrease the elasticity of supply → increase prices → increase defaults → can lead to credit crunches and higher volatility:

  • e.g. South Korea

Part II: A Simple Dynamic Model of the Housing Market:

Based on the stock adjustment model by Malpezzi and Maclennan:
, where  = Quantity demanded, K* = Desired stock,  = Housing stock in the preceding period and = Change in stock

, where P = price, Y = income, N = population
: Quantity supplied as a function of price

-> Q_d = Q_d

  • Simplification for the sake of simulation: lumps population and income as one variable (demand) and price as another

  • D: the amount of stock demand conditional on realized income and population
  • Extension: introduction of time as a main variable to measure temporal lags in supply
    • New supply function: measures quantity supplied in housing based on price and time

  • For notational purposes, assume an order of two supply function, contemporaneous and one period lag:

• Then, substitute
for
Setting Q_d = Q_s
Solve for

However, speculation is generally a demand-side phenomenon, and speculators generally have adaptive expectations. Let’s assume that this is true. Here is an alternative model measuring demand:

  • D is exogenous; is either one-time isolated shock, or rows over time as populations, income or capital stock grows
  • The Simulation Model: Malpezzi and Wachter then developed a simulation model to understand the whether real estate speculation is a factor or a result of the boom and bust cycle. In this model, speculation is linked to housing supply elasticity and to land price volatility. Note that the housing supply elasticity accounts for the effects of land development regulations. This model is important because patterns of financial crises are linked with business cycle downturns. The economies that are most affected quickly undergo an economic downturn/collapse that is usually preceded by a collapse in property prices which then leads into that of banking systems, exchange rate, business cycle bust etc. as seen in Asia: Japan, Indonesia, Thailand
    • Focuses on parameters:
  • Price elasticities of supply:
  • Elasticity of demand with respect to price changes:

o Other parameters:
= price elasticity of demand for housing
= stock adjustment parameter

  • Findings:
    • The simulation model generates cycles with two sources:
      • Since prices are a function of housing stock, new supply and stock is related to current and past prices
      •  as a speculative parameter
    • Inelasticity of supply increases market volatility:
      • Under an inelastic supply case: housing supply does not expand to match changes in demand, and thus prices will rise, especially when investors form adaptive expectation
      • Under an elastic supply case: housing supply expands rapidly to accommodate increases in demand, therefore prices stay relatively constant.
  • Basic conclusions:
    • Even a simple model of lagged supply response to price changes and speculation is sufficient to generate real estate cycles
    • Volatility of prices is strongly linked to housing supply
    • Effect of speculation depends on supply conditions
    • Markets with more responsive regulatory environments (or less issue due to physical geography) experience less speculation
  • Policy implications:
    • Effects of speculation dominated by price elasticity of supply à large effects when inelastic supply à policy to increase supply efficiency where elastic supply can be achieved
    • Demand conditions and speculation à factors in boom and bust cycles (bubbles)
  • Possible extensions
    • Finding alternative lag structures for the supply response
    • Finding better estimates of parameters
    • Finding alternatives to initial adaptive expectations mechanism for formation of housing market expectations

References:

Malpezzi, Stephen, and Susan M. Wachter. “The role of speculation in real estate cycles.” Journal of Real Estate Literature 13.2 (2005): 141-164.

Measuring Speculation in Housing Bubbles

Measuring Speculation in Housing Bubbles By Cecilia Ju

Literature Survey: Measuring Speculation in Housing Bubbles

 

A housing bubble is defined by rapid increases in property values to the point of unsustainable levels followed by a steep decline to the point in which the mortgage debt exceeds the value of the property itself (Bianco, 2008). Just as financial crises are not identified until a downward spiral has occurred, the housing bubble was not recognized until 2006 when market correction was already in stride.

The impact of the housing bubble exceeded experts’ forecasts. As a national average, house prices in the United States grew 6.5% per year in real terms between the late ‘90s and early 2000s (Goodman and Thibodeau, 2008). However, these price growths were especially prominent in cities along the East and West Coasts; California cited an average annual increase of 15% between 2000 and 2005 (Goodman and Thibodeau, 2008). However, the meltdown was quick to reverse the prices just as quickly as they had risen. Most economists believed that the crisis would be contained within the housing market – particularly among mortgage issuers. As it turned out, the subprime crisis that led to the collapse of the housing bubble was the prime factor for the most recent recession, a recession that has spread well beyond the US economy and into economies worldwide. Domestically, the pop of the housing bubble led to a flurry of federal regulations for the financial industry, a drastic decrease in state and local budgets due to a fall in property tax revenues, as well as homelessness by those who have lost their homes to foreclosure or landlord defaults (Bianco, 2008).

While the general public blames speculators for driving prices up to artificial levels, Goodman and Thibodeau (2008) claim that much of the price increase can be attributed to changes in fundamental economic determinants. Across all literature surveyed, researchers recognized that the housing bubble was concentrated along coastal states. Galeser, Gyourko, and Saiz (2008) examine housing supply and housing bubbles in their working paper, and identified elasticity in the housing supply as the main independent variable affecting housing price increase, bubble frequency, and bubble duration. Utilizing data from the two most significant housing market bubbles in the past 25 years, Galeser et al. (2008) constructed a model of housing bubbles with supply as an endogenous variable. During the two most recent housing crises (the 1982-1996 Cycle and the Post-1996 Boom), this model indicated that building infrastructure on steep topography created inelasticity of supply, which created higher price booms (Galeser et al., 2008).

During the ‘80s, areas with elastic supply were hardly affected by the housing bubble occurring in supply elastic places (Galeser et al, 2008). When they did experience housing price booms, the duration of the bubble in these areas was also significantly shorter (Galeser et al, 2008). The model predicted that locations with inelastic housing supply experience greater increases in price compared to those with more elastic supply (all else held constant) (Galeser et al, 2008). Furthermore, inelasticity in housing supply is also positively correlated with bubble frequency and duration (Galeser et al, 2008).

Goodman and Thibodeau’s research also took a supply side approach and supports the idea that expected rate of appreciation in house prices is highly contingent upon housing supply elasticity. However, in their deconstruction of the recent housing bubble between 2000 and 2005, Goodman and Thibodeau also incorporated the effects of demand. Their analysis utilized data to parse out the portion of price appreciation attributable to fundamental economic determinants for house prices.

First, Goodman and Thibodeau (2008) addressed the increase in demand and its effect on homeownership rates: between the years of 1999 and 2006, the rate of homeownership in the US increased from 66.8 percent to 69 percent. While it may seem that the 2.2 percentage point increase is rather unimpressive, it is important to keep in mind that each percentage point in homeownership rate raises demand for owner-occupied housing by approximately one million units (Goodman and Thibodeau, 2008). The increase in demand is also attributed to an increase in real estate investment as well as to speculation in “continued house appreciation” (Goodman and Thibodeau, 2008). In terms of real estate investment, historically low nominal interest rates and the subsequent virtual removal of wealth and income as a barrier to homeownership in the US was cited as a main reason (Goodman and Thibodeau, 2008).  Another reason was the cultural and political shift from renting to homeownership amongst single-family households  (Goodman and Thibodeau, 2008). On the speculative side, rise in demand was attributed to the continuous development of the home-equity market. On the supply side, land prices and housing construction costs increased. The demand for homeownership by households that had historically rented and by preexisting homeowners alike rose at a more rapid pace than did the rise in supply of housing  (Goodman and Thibodeau, 2008). Thus, due to the shortage in houses on the market, the real price of homes increased  (Goodman and Thibodeau, 2008).  A two-pronged approach was applied to answer the question of how much of this appreciation was driven by the justified economic fundamentals of local housing markets and what fraction was driven by speculation. The relationship between house price appreciation rates and supply elasticities were investigated via a simulation model of the housing market and estimates of metropolitan area housing supply elasticities were produced using cross-sectional place data of the non-bubble 1990-2000 period  (Goodman and Thibodeau, 2008).  The empirical analysis revealed statistically significant positive supply elasticities for 84 metropolitan statistical areas (MSAs) (Goodman and Thibodeau, 2008).  Then, using the American Community Survey for 2000-2005 changes, Goodman and Thibodeau (2008) used computed expected rates of appreciation for these MSAs and compared the expected appreciation rates to the rates observed over the 2000-2005 period. The results indicated that the expected rate of appreciation in house prices is very sensitive to the assumed supply elasticity (Goodman and Thibodeau, 2008). Given that 30% of over the expected increase based on the data was used as the housing bubble threshold, only 25 of the 84 metropolitan areas with significantly positive supply elasticities exceed this threshold  (Goodman and Thibodeau, 2008).  Furthermore, these cities, with the exception of Las Vegas, were all coastal and within 75 miles of either the east or west coast.  This led to the conclusion that speculative activity was extraordinarily localized to coastal areas where housing supply was inelastic  (Goodman and Thibodeau, 2008).

Despite strong evidence of a relationship between supply elasticity and housing price increase and stronger speculative pricing effects, Wheaton et al. offers … In their research, Wheaton and Nechayev (2008) also examined the inflation of house prices of the most recent bubble, albeit of a slightly earlier timeframe (1998 – 2005) and investigated its relationship to increases in demand fundamentals (population, income growth, decline in interest rates) over this period. Then, Wheaton and Nechayev (2008) assessed and predicted patterns for housing price correction in the years following 2005.

The research incorporated data of 59 MSAs from 1998 through 2005, and constructed time series models to estimate markets and price changes (Wheaton and Nechayev, 2008). The aforementioned economic fundamentals were utilized to drive the models (Wheaton and Nechayev, 2008). Results from the models found that in all 59 markets, actual price growth between 1998 and 2005 was actually significantly higher than those forecasted (Wheaton and Nechayev, 2008). Wheaton and Nechayev (2008) were able to find that forecast errors are most prevalent in several types of MSAs: larger MSAs, MSAs where second home and speculative buying was prevalent, and MSAs where indicators suggest the sub-prime mortgage market was most active. Wheaton and Nechayev (2008) found two major factors that explained the most recent bubble and the commonly seen “excess” price increase in coastal cities: widespread availability of risk-priced mortgage credit and the unusually strong purchase of houses as second homes and investments. As a caveat, they noted the importance in inferring causality and thus stated that it is difficult to determine depth and duration of the housing correction. Since these factors are all unique to the recent housing market, assessing potential price “correction” after 2005 could not be done without inferring causality (something Wheaton and Nechayev were reluctant to do) (Wheaton and Nechayev, 2008). Thus, determine depth and duration estimates of the housing correction were not formulated.

Most of the literature focuses on identifying reasons behind recent housing booms, presumably for the purpose of understanding, identifying, and avoiding future bubbles. Most of the sources attributed the latest housing bubble to changes in market factors, particularly low interest mortgages and a trend in real estate investment and homeownership. Geospatial analysis of house prices over the time span of the last bubble also revealed a negative correlation between housing supply elasticity and susceptibility to speculative forces. The research reports a lack of optimism in correctly projecting correction terms as well as the nature of the next bubble. Moreover, since such projections and models are based solely on the most recent bubbles, the unique characteristics of the last case may interfere with prediction capabilities. Perhaps an in-depth analysis of the most significant bubbles (domestic and international) from the past century may allow researchers to weed out the unique characteristics of each case and identify several classic traits of real estate bubbles.

 

References

Allen C. Goodman, and Thomas G. Thibodeau. “Where are the Speculative Bubbles in the US Housing Markets?”Journal of Housing Economics 17 (2008): 117-37. Print.

Edward L. Galeser, Joseph Gyourko, and Albert Saiz. “Housing Supply and Housing Bubbles” National Bureau of Economics Research (2008). Print.

Katalina M. Bianco. “The Subprime Lending Crisis: Causes and Effects of the Mortgage Meltdown”  www.consejomexicano.org

William C. Wheaton, and Gleb Nechayev. “The 1998-2005 Housing “Bubble” and the Current “Correction”: What’s Different this Time?” JRER 30 (2008). Print.