The construction sector

Capital Stock Adjustment Models (CSAMs)

• Built real estate (RE) is treated as a capital stock.
• Stock increases with new construction $C<em>t$ and decreases with depreciation $\delta S</em>t$ (both physical & economic).
• Accounting identity: $S<em>t = S</em>{t-1} + (C<em>t - \delta S</em>t)$
• Under constant demand:
• Long-run equilibrium requires $C<em>t = \delta S</em>t$ so that the stock remains constant.
• New construction merely replaces depreciated stock.
• One-time positive demand shock (illustrated via price path $P<em>1 \rightarrow P</em>2 \rightarrow P<em>3$): • Short-run: supply is fixed ⇒ prices rise to $P</em>2$.
• Medium-run: higher prices trigger extra construction → stock shifts $S<em>1 \rightarrow S</em>2$.
• New equilibrium reached at larger stock & lower price P3 < P2 but P3 > P1 because:
• Total depreciation $\delta S_t$ is now larger (bigger base).
• The depreciation rate $\delta$ itself may rise (accelerated economic obsolescence from rapid new supply).
• Reality: continual demand & macro shocks prevent the market from ever settling; equilibrium is a moving target.
• German quarterly residential price series show frequent positive/negative swings.

The DiPasquale–Wheaton (DiPW) Four-Quadrant Model

• Purpose: links space market (rents), asset market (prices), construction market, and the stock of RE.
• Variables
• $S$ – existing stock of space/units (m², ft², etc.)
• $R$ – rent (price for space use)
• $P$ – asset price per RE unit
• $C$ – flow of new construction
• $E$ – exogenous demand shifters (e.g., employment)
• $k$ – discount factor (inverse of capitalization rate) reflecting capital-market conditions & required risk premium.
• Four behavioural equations
• Demand (space market): $R = f(S, E)$ (downward-sloping in $S$, upward in $E$).
• Asset pricing: $P = R / k$ (perpetuity with discount factor $k$; lower $k$ ⇒ higher $P$).
• Construction supply: $C<em>t = f(P</em>{t-1})$ (developers decide today on last period’s observed prices).
• Capital accumulation: $S<em>t = S</em>{t-1} + C<em>t - \delta S</em>t$.
• Graphical mechanics (quadrant walk):
1. Start in NE quadrant with stock $S$ intersecting demand curve ⇒ rent $R$.
2. Move SE: translate rent into asset price via $P = R/k$.
3. Move SW: price motivates construction $C$.
4. Move NW: new construction adjusts stock next period.

Demand-Side Shock Dynamics in DiPW

• Positive shock to $E$ shifts demand curve right → higher $R$.
• Higher $R$ ⇒ higher $P$ (given constant $k$).
• Elevated $P$ ⇒ larger planned construction $C$.
• Greater $C$ expands $S$ next period, dampening $R$ → feeds back to lower $P$.
• Overshooting: because developers base $C<em>t$ on yesterday’s $P</em>{t-1}$, they ignore that future $R$ & $P$ will fall as supply ramps up ⇒ boom-bust pattern in construction volume.

Why Overshooting Occurs

• First-mover / patent-race analogy:
• Competing developers rush to capture peak prices, duplicating investment.
• Expectation formation under uncertainty:
• Herd behaviour & Keynesian “animal spirits” – current price growth extrapolated.
• Lack of granular data leads to naïve forecasting.
• Land scarcity & regulatory bottlenecks concentrate feasible sites, fostering bandwagon development (certain parcels/sublocales lead the surge).
• Capital-market shocks: falling risk premium ⇒ lower $k$ ⇒ price surge even if space demand unchanged → construction boom.

Construction & Real-Estate Cycles

• Extensive empirical evidence: RE investment is cyclical (amplitude & periodicity vary by subsector & region).
• Drivers (shocks):
• Demographics, macro business cycle, credit conditions, regulatory changes, inflation expectations.
• Construction lead times and supply inelasticities convert shocks into multi-year cycles.
• Wheaton (1999):
• US metros 1969-96 → industrial & housing correlate strongly with GDP; retail & office less so.

Pro- vs. Counter-Cyclicality

• Housing construction: clearly pro-cyclical (income & mortgage credit tied to employment & interest rates).
• Other subsectors: mixed; public-works spending can make some construction counter-cyclical during recessions.

Layered Cycles

• Short “demand” cycles ≈ 4–5 yrs nested in medium “supply” cycles ≈ 9 yrs and long urbanization waves (multi-decade).
• Spill-over: shocks in fast-reacting segments/locations propagate to slower ones with lags.

Evidence Snapshots

• US: residential spending (green line) swings more than non-residential.
• Housing starts vs. prices: prices trend upward steadily; construction fluctuates with macro cycle.
• Germany: like US, price volatility < construction volatility at national level.
• Inflation generally pushes prices up but explains only part of variance.
• Post-1990s Germany: falling nominal & especially real interest rates align with rising housing prices.
• Glaeser et al. (2008):
• Bubble amplitude is greater & lasts longer where supply is inelastic (land/regulation).

Regulation & Real-Estate Supply

• RE markets are spatially segmented (Berlin ≠ Oslo) & heavily regulated; thus rarely perfectly competitive.
• Regulatory instruments that curb supply:
• Zoning by use (residential, office, industrial, etc.).
• Physical/form constraints: height limits, setback rules, FAR, energy codes, architectural style mandates.
• Environmental & amenity preservation: green belts, urban parks, protected viewsheds.
• Motivations: safety, amenity value, pollution control, congestion mitigation, technical considerations (e.g., airport flight paths).
• Result: artificial scarcity ⇒ market quantity Q0 < Qc (competitive quantity) ⇒ price P0 > Pc.
• Diagram: vertical supply at $Q<em>0$, demand intersects at $P</em>0$.

Economic Trade-offs

• Higher prices & unmet demand represent welfare loss relative to competitive benchmark.
• Yet regulation can provide externality correction & amenity benefits → optimal regulation set by cost-benefit analysis, not zero regulation.
• NIMBYism: local opposition skews regulation excessively restrictive relative to broader social optimum.
• Example: refuse siting, power plants; locals bear costs, region enjoys benefits.

Case Study: Rome

• High prices despite abundant green/open areas.
• Archaeological & historical preservation restricts new development & building alterations.

Minimum Profitable Production Cost (MPPC) & Tobin-style Measure

• Components of delivering a dwelling:
1. Land cost $L$.
2. Construction cost $CC$.
3. Entrepreneurial profit factor $EP$ (developer’s required rate; ≈ $17\%$ in US, 1985-2013).
• Definition: $\text{MPPC} = (L + CC) \times EP$.
• Price-to-MPPC ratio analogous to Tobin’s $q$ (market value ÷ replacement cost):
• Ratio <1 ⇒ rebuilding uneconomic; vacant lot persists after destruction. • Ratio >1 ⇒ some barrier (regulation, scarcity) supporting prices above cost.

Empirical Findings (Glaeser & Gyourko 2018, US 1985-2013)

• Share of homes with \text{price}/\text{MPPC} > 2 varied dramatically:
• $3.6\%$ in 1985 → $12\%$ (1990) → $5.5\%$ (1995) → $28\%$ (2005) → $\approx13\%$ post-crisis.
• Annual land-scarcity is relatively fixed; hence time-series swings mostly capture regulatory stringency & credit shocks.
• Cross-metro comparison: inverse relation between permitting intensity (share of new permits / existing stock) & avg. price-to-MPPC.
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• Low-permit metros (tight regulation) show high price-to-MPPC ratios (e.g., coastal California, NYC).
• High-permit metros (elastic supply) exhibit ratios closer to $1$ (e.g., Houston, Atlanta).

Implications & Applications

• Developers & policymakers need to understand that:
• Cycles are partly endogenous to supply lags & behavioural expectations.
• Regulation magnifies price shocks where supply cannot expand quickly.
• Monitoring price-to-MPPC (or Tobin-style $q$) provides a diagnostic for excessive regulatory constraint.
• For investors: regions with permanently high price-to-MPPC offer capital-gain potential but higher bubble risk & political scrutiny.
• For social welfare: calibrating regulation requires weighing externality abatement benefits vs. housing affordability costs.

Key References for Further Study

• DiPasquale, D. & Wheaton, W. C. (1992), “The markets for real estate assets and space: A conceptual framework,” Real Estate Economics.
• Wheaton, W. C. (1999), “Real estate cycles: Some fundamentals,” Real Estate Economics.
• Glaeser, E. L. & Gyourko, J. (2018), “The Economic Implications of Housing Supply,” Journal of Economic Perspectives.
• Glaeser, E. L.; Gyourko, J. & Saiz, A. (2008), “Housing supply and housing bubbles,” Journal of Urban Economics.
• Glaeser, E. L.; Gyourko, J. & Saks, R. (2005), “Why is Manhattan so expensive? Regulation and the rise in housing prices,” Journal of Law and Economics.
• Deutsche Bank (2022), “Outlook for the German residential property market 2022 and beyond.”