1 Nonresidential Real Estate © Allen C. Goodman, 2002

2 Review 4-Quadrant Model Real estate is a useful linkage to land use theory and actual land usage. Demand for real estate (use of space) comes from occupiers of space. They are willing to rent the use of space. For firms, space is a factor of production. For households space is a commodity Supply of real estate comes from the construction sector and depends on the price of those assets relative to the cost of replacing or constructing them.

3 Two explicit linkages 2 explicit linkages between the asset market and the property market –Rent levels in the property market are central in determining the demand for real estate assets. –In the construction or development sector – if construction increases and the supply of assets grows, not only are prices driven down in the asset market, but rents decline in the property market as well.

4 Four Quadrant Model (1)R = 40 – (S/10E) (2)C = (P – 200)/5 (3)S = 100 C, and (4)P = R/i (5)S = [E/(iE +2)][800 –4000i] If E = 10 m; i = 0.5, S = 240 sq.ft./worker  24 m sq.ft. of C/yr.  Rents = $16/sq.ft.  P = $320/sq.ft. Rent R ($) Stock S (sq. ft.) Price P ($) Construction C (sq. ft.) P = R/ C = (P – 200)/5 R = 40 – (S/10E) S = 100 C

5 Four Quadrant Model Suppose  falls from 0.05 to Q2 line rotates. New equilibrium has to satisfy other conditions. Price rises. Stock increases. Rent falls. Trace it through. Rent R ($) Stock S (sq. ft.) Price P ($) Construction C (sq. ft.) P = R/ 0.05 C = (P – 200)/5 R = 40 – (S/10E) S = 100 C P = R/ 0.04

6 Demand Shift (1)R = 40 + z – (S/10E) (2)C = (P – 200)/5 (3)S = 100 C, and (4)P = R/i S >240 sq.ft./worker  >24 m sq.ft. of C/yr.  Rents > $16/sq.ft.  P > $320/sq.ft. Rent R ($) Stock S (sq. ft.) Price P ($) Construction C (sq. ft.) P = R/ C = (P – 200)/5 R = 40 – (S/10E) S = 100 C R = 40 + z – (S/10E)

7 Natural Vacancy Rates Comes from inventory theory What are inventories? Why do firms keep them? –Orders and production are not evenly matched. Why don’t they like to keep them? –Costs $ to hold them, guard them, maintain them. $ could be used elsewhere.

8 How does this fit in for RE Landlords hold vacant space to satisfy needs of demanders. It’s optimal to hold a certain amount of vacant space for the same reason it’s optimal for merchants to hold inventory. What are landlords’ costs? –Property taxes –Interest on the mortgage –Insurance –Security

9 Suggested relationships  R = f (V A – V N ); V A = actual; V N = natural As (V A – V N ) ,  R . Key part of the argument is that rents are slow to change. Why? Long term leases (typically 3 – 15 years on the office market) High transactions and search costs, particularly for buyers.

10 Supply is slow to adjust Additions to office space stock are no more than 1 – 2% per year. If vacancy rate V = 0.05S, and C = 0.01S, then: C/V = 0.20  sizable, but not instant.

11 Possible relationship V N = f (  D, T p, T I, I, r, i) V N = Natural vacancy rate  D = Change in demand (+, because landlords can reasonably expect higher returns from keeping the property off the market. T I = Income tax rate (+. Because tax shelter aspect of real estate  more office investment) Tp, I, r = Property tax, insurance, interest rate (-, because cost of holding idle resources  ). i = inflation (+, generally, because of assumption that real estate is a good hedge against inflation).

12 Possible relationship Vacancy gap G G = V N – V A. Changes in rental rates,  R, are functions of inflation i and the vacancy gap G.  R = f (i, G) i  nominal terms G  real terms Let’s draw a picture

13 Vacancy Dynamics Start at eq’m at which vacancy rate = natural rate. This is consistent with a constant rate of construction, to replace buildings that wear out. Change in real rent,  R Vacancy rates, V N and V A Construction C as a ftn of S  R = f (G) C = h(  R) VNVN V A = +-

14 Vacancy Dynamics Suppose we have a shift in QI of the 4-Quad diagram.  Decrease in V below the natural vacancy rate. This tends to push up real rent so  R . Increase in  R will eventually lead to increases in construction. Since construction rises above replacement level, stock eventually  from S 1 to S 2. Change in real rent,  R Vacancy rates, V N and V A Construction C as a ftn of S  R = f (G) C = h(  R) VNVN VNVN V A = VAVA G

15 Vacancy Dynamics Since construction rises above replacement level, stock eventually  from S 1 to S 2. Vacancy rate  and G , reducing growth of real rents. Change in real rent,  R Vacancy rates, V N and V A Construction C as a ftn of S  R = f (G) C = h(  R) VNVN VNVN V A = VAVA G Eventually V A  V N,  R  0. New construction  replacement percentage, but replacement level is associated with new, higher stock of space S 2.

16 Vacancy Dynamics What about negative demand shock. Key point is that minimum construction is 0. Change in real rent,  R Vacancy rates, V N and V A Construction C as a ftn of S  R = f (G) C = h(  R) VNVN V A = VAVA G So, when demand , office mkt may be left w/ high vacancy and declining rents for a while. Must wait for depreciation, and + growth in demand

17 We’ve concentrated on demand shocks Supply shocks –In mid to late 1980s cost of financing new office construction decreased. –Interest rates fell. –Lots of tax shelters led to a lot of building, possibly overbuilding.

18 Estimating V N Does V N change or doesn’t it? For a city,  R = b 0 + b 1  E – b 2 V +   R = change in base rent/sq.ft.  E = change in operating expenses V = actual vacancy rate If we estimate this city by city then we can get a “natural” rate.

19 Estimating V N  R = b 0 + b 1  E – b 2 V +   R = change in base rent/sq.ft.  E = change in operating expenses V = actual vacancy rate Schilling et al. found rates varying from 1% (New York City) to over 20% (Kansas City) Rate was low in Chicago, San Francisco, Atlanta Rate was high in Portland, Spokane, Pittsburgh WHY so different? Central tendency through 70s and 90s was 8 – 9%. RR V Set  E = 0, or  E = constant Solve for V such that  R = 0!

20 Bargaining Model Wheaton/Torto use a “bargaining model.” Argue that rents are set in bargaining between landlords and tenants. More tenants  higher optimal rental rate by landlords. Large amount of vacant space  more tenants opportunities and lower optimal rental rate. Here rental rates adjust rather than vacancy rates. No role, really for new construction to influence rents.

21 Other critiques Distinction between vacant and abandoned properties is sometimes artificial. Vacant properties exert market pressure – abandoned ones do not. If some cities are more strict about tearing down abandoned buildings, their vacancy rates will look low, when they’re really not. Need to model shifts in the structure of relationships that produce V N. What if office tenants are moving to suburbs? Leases are becoming shorter and tenant turnover is becoming higher. This would tend to lower V N. Many metro areas have initiated slow-growth policies to limit office growth. Would tend to limit adjustment.

22 Recent Research – San Francisco FED Table 1 Natural Vacancy Rates, 2001.Q2 Estimated naturalActualvacancy rate Boston Houston Los Angeles Phoenix Portland Salt Lake City San Francisco Seattle Krainer