Home » 2014 Categories » 2014 Technical Presentation » The Role of Speculation in Real Estate Cycles

# 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)

$V=\sum_{t=0}^T\frac{E[R_t-C_t]}{(1+i)^t}$

• 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:

$V\cong&space;\frac{E[R]}{i}$
o If we account for a property that has increasing value:
$V\cong&space;\frac{E[R]}{i-g}=\frac{E[R]}{c}$
• c: cap rate: the rate of return on a real estate investment property based on the expected income the property will generate.
$c=\frac{yearly&space;income}{total&space;value}$

• 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
$V_t=V^{*}_t+b_t$
where $b_t$ = overvaluation amount
$E_t[b_t+1]=(1+i)b_t$
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:
$Q_D=\delta&space;(K^{*}-K_{-1})$, where $Q_D$ = Quantity demanded, K* = Desired stock, $K_{-1}$ = Housing stock in the preceding period and$\delta&space;(K^{*}-K_{-1})$ = Change in stock

$K^{*}&space;=&space;\bar{a}+\alpha_1P+\alpha_2Y+\alpha&space;_3N$, where P = price, Y = income, N = population
$Q_s&space;=&space;\bar{\beta&space;}+\beta&space;_1P$: 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

$K^{*}&space;=&space;D&space;+&space;\alpha&space;_1P_1,&space;\alpha&space;_1<0$

• 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

$Q_{st}=\beta&space;_0P_t+\beta&space;_1P_{t-1}+B_2P_{t-2}+...+B_nP_{t-n}$

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

$Q_{st}=\beta&space;_0P_t+\beta&space;_1P_{t-1}$

• Then, substitute
$K^{*}$ for $Q_d$
Setting Q_d = Q_s
Solve for $P_t$
$P_t=\frac{\beta&space;_1}{\beta&space;_0-\delta&space;\alpha&space;_0}P_{t-1}+\frac{\delta}{\beta&space;_0-\delta&space;\alpha&space;_0}D-\frac{\delta}{\beta&space;_0-\delta&space;\alpha&space;_0}K_{-1}$

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:
$K^{*}=D+\alpha&space;_1P+\alpha&space;_4dP,&space;\alpha&space;_1<0,&space;\alpha&space;_4>0$

• 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:$\beta_i$
• Elasticity of demand with respect to price changes:$\alpha_4$

o Other parameters:
$\alpha_1$ = price elasticity of demand for housing
$\delta$ = stock adjustment parameter

$K_{t}^{*}=D_t+\alpha&space;_1P_{t}+\alpha&space;_4(P_t-P_{t-1})$

$P_t=\frac{\beta&space;_1}{\beta&space;_0-\delta&space;\alpha&space;_0}P_{t-1}+\frac{\delta}{\beta&space;_0-\delta&space;\alpha&space;_0}K_{t}^{*}-\frac{\delta}{\beta&space;_0-\delta&space;\alpha&space;_0}K_{t-1}$

$Q_s=\beta&space;_0P_t+\beta&space;_1P_{t-1}$

$K_t=K_{t-1}+Q_s$

• 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.