The Construction Sector

Real Estate Economics: The Construction Sector

Capital Stock Adjustment Models (CSAMs)

Definition and Components

Capital stock adjustment models (CSAMs) consider built real estate as a form of capital. The stock of real estate increases with new constructions and decreases due to depreciation, which accounts for both physical and economic factors. The existing stock of real estate at any time t can be represented as:

St = S{t-1} + (Ct - \delta St)

Where:

S_t is the stock of real estate at time t.
C_t is the new construction volume at time t.
\delta is the depreciation rate.

In a state of constant demand, the supply reaches a constant stock level. To maintain this constant stock, the new construction volume C must equal the depreciation.

Market Dynamics

Initial Equilibrium: Starting from an initial equilibrium at price P1, an increase in demand causes real estate prices to rise to P2 (where P2 > P1). This is because the supply is relatively fixed in the short run, as increasing the available real estate stock through additional construction takes time.
Price Adjustment: The real estate stock increases until a new equilibrium is reached, resulting in lower prices P3 (where P3 < P_2).

Long-Term Equilibrium and Depreciation

In principle, a one-time demand shock would gradually bring the market to a new long-term equilibrium where the rate of construction equals the rate of depreciation. However, the new rate of depreciation is likely larger because:

Increased Stock Size: The size of the stock has increased, so even with a constant rate \delta, total depreciation \delta S_t is larger.
Increased Depreciation Rate: The rate \delta will likely be larger due to the acceleration caused by the increase in demand, which accelerates the economic depreciation of existing real estate.

Thus, the new long-term equilibrium is set to be at a higher price than before the increase in demand and at a larger rate of construction.

The Ideal vs. Reality

The ideal of a long-term stable equilibrium is purely theoretical due to frequent changes in demand and various economic shocks. It is more accurate to consider a tendency toward an equilibrium that is rarely reached. Data on percentage change on the previous year of residential property prices can be found at Statista.com.

DiPasquale-Wheaton (DiPW) Model

The DiPasquale-Wheaton (DiPW) model provides a conceptual framework to link the real estate market, the construction market, and capital markets.

Key variables:

S = stock of real estate
R = rent
P = price per real estate unit
C = construction

Model Quadrants

The DiPW model consists of four quadrants:

Demand: Represents demand based on the real estate stock S and other exogenous factors.
Price Determination: Determines the price per real estate unit (e.g., per m^2) as a function of exogenous capital market conditions, such as P = R/k.
Investment Planning: Based on the price P observed in the previous period, developers plan their investments.
Stock Update: New construction is linked to the real estate stock as St = S{t-1} + (Ct - \delta St).

Demand Shock

Starting from an equilibrium where construction equals depreciation, the four quadrants are interconnected. After a demand shock, rents rise, which causes upward pressure on prices P. This increase in prices triggers more construction, which increases the stock S. The increase in available real estate reduces rents, which in turn reduces prices, and the cycle continues.

DiPW Model Equations

The DiPW model is based on four equations:

R = f(S, E)
P = R / k
Ct = f(P{t-1})
St = S{t-1} + Ct - \delta St

Where:

E represents exogenous factors driving demand (e.g., employment for manufacturing real estate).
k is a capital discounting factor.

These four equations can be separately estimated and used to provide the model with predictive power.

Exogenous Changes

The DiPW model can also accommodate other types of shocks. For instance, an exogenous change in financial markets may affect the discounting factor k and/or the depreciation factor \delta. This means that movements (and possible cyclicalities) in the construction and real estate sectors can be induced by changes over time in demanded risk premia.

For example, in periods of economic boom when investors are highly confident, risk premia may go down, thus reducing k and increasing prices P. This may trigger an increase in construction even if real estate demand was not initially affected.

Overshooting of New Construction

The DiPW model suggests that developers act as if they do not realize that prices will adjust again, leading to an overshooting of new construction.

Reasons for overshooting:

Patent Race Analogy: Real estate construction may be excessive, similar to a "patent race," where multiple entities invest in the same research project hoping to be the first to achieve a patent grant and thus enjoy monopolistic profit. This produces overinvestment as the same R&D efforts are duplicated. Similarly, developers might hope to gain a first-mover advantage in being among the first to sell at the highest price.
Uncertainty: Uncertainty about the final market equilibrium. Formation of expectations under uncertainty can be influenced by herd behavior (i.e., observing what others do) and by “animal spirits” (in the form of excessive confidence that the market will grow). Also, under uncertainty and with a lack of more dependable information, proxies such as the current price or rate of growth may be used to gauge future prices and rates.
Land Scarcity: Because of land scarcity, often development happens in a “bandwagon” fashion: first some specific sites are developed (e.g., office, mall, and hotel sites that are quicker at reacting to price signals), then in time other vacant sites are developed. Such scarcity may also be due to regulations and not to physical scarcity.
Changing Discounting Factor: The discounting factor k may change, as demanded risk premia fall with the rise of real estate values.

Mitigating Factors

Taken together, these explanations imply that developers will likely use current prices and growth rates to foresee future market changes. Hence, overbuilding may occur. Such overbuilding is somewhat mitigated however by land scarcity, credit constraints, and limited construction resources.

The relative strength of these limiting factors may help explain why bubble-and-bust dynamics only happen in specific places and times, and not always when a positive demand shock occurs.

Cycles in the Real Estate and Construction Markets

Cyclical Behavior

Data and empirical research indicate that real estate investment displays cyclical behavior. Many factors contribute to this cyclicality, including demographics, regulation, the business cycle, inflation, and financial aspects. These factors may provide shocks that initially trigger a cyclical adjustment, such as construction investments overshooting and subsequent adjustments (as seen with the DiPW model).

Sub-sector Variations

Sub-sectors may behave differently. Wheaton (1999) studied the US metropolitan RE market in 1969-1996 and showed that the industrial and housing markets correlated significantly with the general economy, while the retail and office markets did not.

Procyclicality and Counter-cyclicality

Even when a RE market correlates with the rest of the economy, the question remains: do cycles move in the same or in opposite direction as the general economy? And with ampler or smoothed amplitude?

The housing construction market seems to be definitely procyclical. Demand for housing depends on households’ income and mortgages, which are obviously affected by unemployment rates and credit conditions.
For other RE sub-sectors it is less clear cut.

Governments often try to stimulate a slow economy via public spending, often for construction or renovation of existing RE. This may cause construction activities (in the subsidized sectors) to display counter-cyclical behavior.

Multiple Cycles

Considering the RE market as a whole would mask internal heterogeneity. Cycles appear to differ based on the observed subsector (housing, offices, industrial, etc.). Moreover, shorter “demand” cycles (4-5 years of length) can be nested into longer “supply” cycles (up to 9 years) and even longer “urbanization” waves that are linked to phenomena spanning across multiple decades.

Cycles can be linked. A rise in construction activities in a more reactive sector or area may spill over another, less reactive sector or area in a subsequent period, thus causing a propagation of a shock leading to lagged and smoothed cycles.

Empirical Evidence

US Construction Spending: Residential construction appears more sensitive to the business cycle.
US Housing Prices and Construction: New housing prices follow an upward long-term trend, while construction investment follows the ups and downs of the general economy.
German Housing Market: Prices vary less than new construction supply at the national level.
Inflation and Housing Prices: Inflation is on average associated with higher housing prices, but it only explains a fraction of the variation in housing prices over time.
Interest Rates and Housing Prices: Lowering interest rates in Germany are associated with rising housing prices, especially since the end of the 1990s.

Glaeser et al. (2008) Model

Glaeser et al. (2008) presented and estimated a model that accounts for housing demand and supply, based on US metropolitan area-level prices (expressed as constant 2007 prices) between 1982 and 2007. The main result is that housing prices increase more during bubbles in areas where housing supply is inelastic.

Places with less stringent scarcity of developable land (either because of fewer physical and/or regulatory constraints) will have fewer, faster, and less dramatic housing bubbles. By the same token, areas where supply is more inelastic will experience bubbles more frequently and these will also last longer.

Regulation and Real Estate Supply

Market Competition

In textbook economics, we study competitive markets where the equilibrium price of a good equals the marginal cost of producing the last unit. Real estate markets are never fully competitive because:

Markets are segmented by location: housing supply in Berlin hardly competes with housing supply in, say, Oslo.
Regulation.

Types of Regulation

RE regulation may limit development of new RE units in many different ways:

By constraining an area only for a specific type of use, e.g., only for housing, only for offices, etc.
By imposing additional building requirements, e.g., a maximum building height, minimum distances between buildings, energy consumption requirements, aesthetical requirements (e.g., the requirement to comply with a specific architectural style), and so on.
Development may be constrained for environmental concerns, such as green belts (rings of land that extend around city blocks and are left unbuilt) and large city parks.

Reasons for Regulation

Regulation exists because of several distinct reasons and objectives: to preserve amenities, to provide added safety, for technical reasons (e.g., lower buildings near airports), to reduce negative spillovers (especially in the form of pollution and congestion).

Consequences of Regulation

Regulation limits the possibility to develop new constructions, acting as an artificial source of scarcity. This prevents local RE markets (in most cities) from ever reaching a supplied quantity of dwellings that would correspond to a competitive market equilibrium, keeping prices above marginal costs.

Impact on Prices and Quantity

With a fixed demand for RE and constrained supply, the market equilibrium produces larger prices:

Pc and Qc are the price and quantity corresponding to a theoretical competitive market.
Because of regulation, supply is constrained to quantity Qc which is associated with price Pc.

Minimum Profitable Production Cost (MPPC)

The use of marginal costs to analyze RE markets is suitable in cases where land is almost fully developed and new construction mostly happens by raising the height of existing buildings or by demolishing old buildings and replacing them with taller constructions (e.g., Manhattan in New York, see Glaeser et al. (2005)).

Because building is mostly a “bulk” investment where many dwellings are developed at the same time, the concept of “minimum profitable production cost” (MPPC) is a more general definition than marginal cost. Pay attention to the difference between price and MPPC, rather than between price and marginal cost.

Tobin's q

The ratio of price to MPPC is akin to Tobin’s q. Tobin’s q is the ratio of market value to firm replacement cost. It is a measure of a firm’s value and, specifically, of its intangible assets’ value and future prospects of profitability.

Regulatory construction constraints can help explain why the ratio of price to MPPC is larger in some RE markets, similarly to how the conditions observed on capital markets may explain variation in Tobin’s q. When the price-to-MPPC is larger, holding other characteristics constant, this signals a more stringent regulation limiting further RE development.

Welfare Implications

Standard economic thinking would lead to believe that RE regulation is welfare-deteriorating because prices will be larger and fewer demands will be satisfied than in a situation with less restrictive regulation. Regulation may also cause additional social costs, for example by restricting the availability of new housing near production plants, which could drive wages up.

However, regulation is often useful and even necessary to preserve amenities. Also, production sites pollute, and a segregation between residential and industrial areas is warranted to preserve public health. Thus, the “optimal” amount of regulation should stem from a cost-benefit calculation that takes into account all effects ascribable to regulation, not just those that are market-related.

NIMBYism

Sometimes regulation originates from locals not wanting to suffer the costs of a land use, the benefits of which fall on a larger geographical area. Examples are power plants, waste management sites, and highways.

So-called NIMBYism (“not in my back yard”) refers to opposition by residents to proposed developments in their area. With NIMBY, regulation can be excessively restrictive from a social perspective, where the point of analysis is the welfare of the average citizen in a larger area encompassing those where NIMBY happens.

Minimum Profitable Production Cost (MPPC) Defined

There are three components to the cost of delivering a unit of housing to the market:

The land (L) on which the housing unit sits.
Construction costs (CC) associated with rising structure.
A rate of entrepreneurial profit (EP) needed to compensate the developer.

Define the “minimum profitable production cost” (MPPC) of a unit of housing as follows:

MPPC = (L + CC) \times EP

For the U.S. in the period 1985-2013, for example, Glaeser and Gyourko (2018) estimate the average return, EP, to be around 17%.

Price-to-MPPC Ratio

The ratio of price-to-MPPC can be calculated for different areas and building types. A value of the ratio below one implies that the capital would not be replaced if it were destroyed. Values of the ratio above one must reflect some barrier to investment, which includes those due to regulation (but also other factors, e.g., physical land scarcity). Glaeser and Gyourko (2018) applied this approach to U.S. housing.

Time-Series Analysis of Price-to-MPPC Ratios in the U.S.

The table shows that the percentage of homes with particularly large price-to-MPPC ratios (above 2) change quite visibly from year to year: as low as 3.6% in 1985, then raises up to around 12% in 1990, then drops to 5.5% in 1995 to rise again up to 28% in 2005, only to rapidly fall again to about 13% in the aftermath of 2007’s financial crisis.

because physical land scarcity hardly changes from year to year, for the same area the price-to-MPPC ratio provides a fair measure of the impact of regulation on RE supply.

Comparisons across different areas need more sophisticated econometric techniques to control for differing land constraints and other characteristics.

Regulation and Construction Permits

Metropolitan areas with a smaller share of permits are also those with the largest average price-to-MPPC, signaling that constrained development pushes prices upward.

Real Estate Economics: The Construction Sector

Capital Stock Adjustment Models (CSAMs)

Capital Stock Adjustment Models (CSAMs) treat built real estate as capital. The real estate stock (St) at time t is St = S{t-1} + (Ct - \delta St), where Ct is new construction and \delta is the depreciation rate. In equilibrium, new construction equals depreciation. An increase in demand raises prices, stimulating construction, which eventually increases stock and lowers prices, though the new long-term equilibrium price and construction rate are typically higher due to increased stock and accelerated economic depreciation.

DiPasquale-Wheaton (DiPW) Model

The DiPasquale-Wheaton (DiPW) model links real estate, construction, and capital markets through four quadrants:

Demand: Based on real estate stock (S) and exogenous factors (E).
Price Determination: Price (P) is a function of rent (R) and a capital discounting factor (k), i.e., P = R/k.
Investment Planning: Developers plan investments based on previous period's price.
Stock Update: New construction (C) updates the real estate stock (St = S{t-1} + Ct - \delta St).

Demand shocks increase rents and prices, triggering construction, which then increases stock, reducing rents and prices, creating a cycle. The model suggests construction overshooting due to factors like a "patent race" analogy, market uncertainty (herd behavior, animal spirits), land scarcity, and changes in the discounting factor.

Cycles in the Real Estate and Construction Markets

Real estate investment shows cyclical behavior influenced by demographics, regulation, business cycles, inflation, and financial aspects. Sub-sectors vary; industrial and housing markets often correlate with the general economy, while retail and office markets may not. Housing construction is generally procyclical. Government spending can induce counter-cyclical behavior in subsidized sectors. Cycles differ by sub-sector and can be nested (shorter demand cycles, longer supply cycles, urbanization waves). Empirical evidence shows residential construction's sensitivity to business cycles, and the Glaeser et al. (2008) model indicates that housing prices increase more during bubbles in areas with inelastic housing supply.

Regulation and Real Estate Supply

Real estate markets are not fully competitive due to segmentation by location and regulation. Regulation limits new construction through zoning, building requirements (height, aesthetics, energy), and environmental concerns (green belts). While regulation preserves amenities, safety, and reduces negative spillovers, it also creates artificial scarcity, keeping prices above marginal costs. The "minimum profitable production cost" (MPPC) (MPPC = (L + CC) \times EP) is often more relevant than marginal cost for analyzing bulk real estate investments. The price-to-MPPC ratio, analogous to Tobin's q, indicates barriers to investment, including regulation. A ratio above one suggests investment barriers, and higher ratios often correlate with stricter development constraints. While regulation can be welfare-deteriorating by increasing prices and restricting supply, it's necessary for public health and amenities. NIMBYism ("not in my back yard") can lead to excessively restrictive regulation. Time-series analysis for the U.S. shows the price-to-MPPC ratio fluctuates, reflecting the impact of regulation, as physical land scarcity remains relatively constant. Metropolitan areas with fewer permits typically have higher average price-to-MPPC ratios, confirming that constrained development inflates prices.