MIT OpenCourseWare
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11.433J / 15.021J Real Estate Economics
Fall 2008
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Week 11: Real Estate Cycles and
Time Series Analysis
• The dynamic behavior of the 4-Q model:
stability versus oscillations.
• Real Estate Pricing Behavior: backward or
forward looking?
• Development Options and Competition
• Forecasting markets: Univariate analysis,
Vector Auto regressions, structured models.
• The definition and evaluation of “risk”
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What are Real Estate “cycles”
• A reaction to a “shock” in the underlying
economic demand for the property: national or
regional recessions and economic boom periods.
[e.g. single family residential, industrial,
apartments]
• A periodic “overbuilding” of the market - excess
supply – that originates from capital or
development activity and is not necessarily linked
to demand movements. [e.g. office, hotels, retail?]
• Which markets/property types exhibit which? Are
Markets changing?
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3.2
4
2.8
3
2.4
2
2.0
1
1.6
0
1.2
-1
0.8
-2
0.4
-3
0.0
New Jobs (L)
Sources: BLS, BOC, TWR.
Total Housing Starts (R)
Total housing starts,
millions of units
5
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year-over-year change in total
employment, millions
Prefect Historic correlation between economic recessions
and Housing Production – except for the last 5 years
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National Office Market:
Completions Rate, Real Rent, Job Growth
$ Per Sqft
Forecast
34.00
8.00%
32.00
6.00%
30.00
28.00
4.00%
26.00
2.00%
24.00
0.00%
22.00
Total Employment Growth (L)
Real Rent (R)
Completion Rate (L)
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
20.00
1971
-2.00%
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National Hotel Market (full service):
Supply Growth Rate, Real ADR, Job Growth
8.00%
Forecast
110.00
Total Employment Growth (L)
Real ADR (R)
Supply Growth Rate (L)
2007
2006
2005
2004
2003
2002
2001
2000
1999
80.00
1998
-4.00%
1997
85.00
1996
-2.00%
1995
90.00
1994
0.00%
1993
95.00
1992
2.00%
1991
100.00
1990
4.00%
1989
105.00
1988
6.00%
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Dynamic 4-Q Model (t=time Period)
1). Office Demandt = α1EtRt-β1
Et= office employment
Rt = rent per square foot
β1 = rental elasticity of demand:
[%change in sqft per worker/% change
in rent]
2). Demandt = Stockt = St [Market clearing]
3). Hence: Rt = (St/ α1Et)–1/β1
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4). Office Construction rate:
Ct-n/St = α2Ptβ2
Pt = Asset Price per square foot
β2 = price elasticity of supply:
[Perfect competition “Q” investment theory
with n-period delivery lag. Projects begun
n-periods back are based the “expected”
value of asset prices at the time of delivery.]
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5). Replacement version:
E= fixed
St/St-1= 1- δ + Ct-n/St-1
6). Steady Demand growth version:
Et = [1+ δ] Et-1
St/St-1= 1 + Ct-n/St-1
[δ can represent the sum of employment
growth and replacement demand]
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7). Myopic (backward) behavior:
Pt = Rt-n / i
i = interest rate (discount rate)
[Extrapolate the future from the current/past]
8). Forward looking behavior (the efficient
market theory):
Pt = ∑Rt’/(1+i)t’-t
t’=t+1,∞
or: Pt+1 – Pt = i Pt - Rt (if R is changing)
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9). Market steady state solution: all variables
are constant over time or are changing at the
same rate as Et grows.
St/St-1= 1+ δ
δ = Ct-n/St-1 ,
Pt = (δ/α2)1/β2, Rt = iPt+1 = iPt with
either pricing model
10). What happens if Et increases “Randomly”
for one period – and then resumes its long
term growth rate? Or interest rates decline?
11). Much depends on the pricing model.
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Valuing Property:
Efficient Asset Pricing Principles
• Use future rent and income forecasts that are based
upon the “model” (i.e. assume all market participants
use the “model” to evaluate the change in E)
• Future Residual values are DCF from the residual
date forward
• Since today’s value is DCF until residual date plus
residual value, the hold period is irrelevant: today’s
value is the in-perpetuity DCF.
• Result: Price Volatility low and less than income
volatility since income volatility is only temporary
(with mean reversion)
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Valuing Property:
The traditional way (Myopic).
• Extrapolate current, past or “average”
rent/income growth (what’s wrong
with ARGUS?)
• Residual value is capped Future NOI
with cap at 50bps over initial period.
• Result: Price Volatility High and
greater than income volatility.
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Implication: when are Cap rates lowest?
• FINANCE 101: Cap rate = i-g+r
i = risk free rate + capital expenditures
g = expected future value/income growth
r = real estate risk premium
• Efficient prices are lowest when the market is
most down. With mean reversion, that’s when
expected rent/income growth is highest!
• With traditional or extrapolative pricing, cap
rates are lowest when the market is strongest
(continued rent/income growth) and highest when
the market is down (continued decline).
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180
9.50
9.00
8.50
8.00
7.50
7.00
6.50
6.00
5.50
160
140
120
100
80
78
80
82
Cap Rate
84
86
88
90
92
Capital Value Index (R)
94
96
98
NOI Index (R)
Index, 1977:4 = 100
Cap Rate (%)
Efficient Market: Prices less volatile than income, cap rate
is low when market is down (mean reversion).
Inefficient Market: Prices more volatile than income, cap
rate is high when market is down (extrapolation).
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The “Shiller Test”
• If market pricing is “rational” cap rates should correlate
negatively (if imperfectly) with actual future (subsequent)
growth. Efficient markets can at least partially anticipate the
future.
• Why weren’t cap rate spreads higher in the late 1980s
anticipating the tanking of the market, and lower in the
early 1990s – anticipating the market recovery?
• The implied growth in today’s cap rates: g (3.5%) =
i (5.0%) + Capex (2.0%) + r (3.0%) – Cap (6.5%)
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Pricing seems always based on expected growth that just
matches inflation - not actual subsequent appreciation
(good for 100 years, but not 10!)
Percent
20
1991: 5.7%=7.8%+5.0%-7.1%
2004: 2.0%=4.5%+5.0%-7.5%
15
10
5
0
-5
TWR Forecast
-10
implied future growth
US Office Investment data.
3-Year Forward Appreciation
CPI inflation
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
-15
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Hence across the EU, 2000 cap rates did not reflect
subsequent (2001-2005) growth in Fundamentals
European Cities, Average Annual Office Rent Growth % 2000q4 - 2005q4
8
Brussels
6
Degree of Under Pricing
4
2
0
-2
-4
-6
Paris
-8
4.5
5.5
6.5
7.5
8.5
2000 q4 Prime Yield
Source: Torto Wheaton Research
9.5
10.5
11.5
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Impulse response to demand shock (increase in E) with
“stable” market parameters. Holds for efficient pricing
and may hold for extrapolative pricing: Intrinsic “mean
reversion”
Market reaction to a 50% demand shock (lag: n = 5; depreciationgrowth: δ = 0.05: demand elasticity = 1.0; supply elasticity = 1.0).
600
Price
Stock/Employment
800
500
600
400
300
400
200
200
100
0
0
10
20
30
40 50 60 70
Period from shock
80
90
100
0
0
10
20
30
40 50 60 70
Period from shock
80
90
100
Figure by MIT OpenCourseWare.
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Impulse response to demand “shock” with “unstable”
market parameters. Holds only for extrapolative pricing
under certain situations: “mean over-reversion”.
Market reaction to a 50% demand shock (lag: n = 8; depreciationgrowth: δ = 0.05: demand elasticity = 0.4; supply elasticity = 2.0).
600
Stock/Employment
1200
500
900
400
600
300
200
300
100
0
Price
0
10
20
30
40
50
60
70
Period from shock
80
90
100
0
0
10
20
30
40 50 60 70
Period from shock
80
90
100
Figure by MIT OpenCourseWare.
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What makes the model unstable?
• More elastic supply (β2). and less elastic demand (β1).
• A high rate of demand growth or rapid obsolescence of
properties (δ)
• Long Delivery lags (n), “slow adjustment”, delayed
responses (regulation?)
• Extrapolative (backward) as opposed to forward,
efficient expectations by investors/developers.
• Variation by property type?
• In any case, all models above have “mean reversion”
and are not a random walks [Shiller].
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Historic Office Rent Volatility by Market:
“Barriers to Entry” = more volatility!
(Barriers = lower supply elasticity or longer lags?)
140
120
100
ATL ANT
B O S TO N
P HO E NI
S F R ANC
80
60
40
20
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
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Historic Real Estate Price volatility: 1978-1998
By Property Type (Source: NCREIF)
205
185
165
145
125
105
85
78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
R&D
Office
Retail
Warehouse
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What if market participants “delay”?
“Slow adjustment” increases instability
• Gradual adjustment of space demand to changes in
employment and rents. Why? Only 20% or so of tenants
can move each year given lease contracts.
• Gradual adjustment of rent to vacancy. Lease contracts
make the leasing decision like an “investment” – there are
option values to both parties to waiting [Grenadier].
Waiting pushes the supply response further into the future
• Example: the “Rental Adjustment” process.
Rt-Rt-1 = λ0 - λ1Vt (Rosen, 1980s).
λ0 / λ1 = “structural vacancy rate”
Rt-Rt-1 = λ0 - λ1Vt - λ2Rt-1 (Wheaton, 1990s).
R* = (λ0 - λ1Vt )/ λ2 “rent at which landlords
indifferent to leasing versus waiting” (R constant)
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“Waiting” = Development as a Real Option
• Competitive model (Tobin’s Q): develop as soon
as when Prices equal replacement cost.
• But what if prices are stochastic, uncertain?
• If wait and they go down – little lost.
• If wait and they go up – a lot gained!
• Hence wait. Until Prices cross a “hurdle” =
replacement cost + “option value” = exercise price
• Greater uncertainty = higher option value = longer
delay to development since exercise price is
higher.
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Development Options and Development Lags
• Lags are delays between when you exercise the option
(commit) and when you realize the Price.
• Lags mean that the impact of uncertainty on the option
value is less than without lags (develop sooner).
– The option value of waiting is less because if good times occur,
and it take you several years to build, by the time you build they
may have vanished. Without lags you can immediately realize the
good times value!
• However, for a given level of uncertainty the hurdle value
is higher with lags than in a model with no construction lag
(develop later).
– Intuitively, the further into the future the realization of your
investment return, the smaller its present value. Therefore, you
optimally wait until the Price is higher (all else equal) in order to
commence development when there are lags between exercise and
realization of Price as opposed to instantaneous realization of Price
upon exercise.
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Development Options and Overbuilding
• When we all wait, its more likely that multiple players
exercise the option at the same time.
• Exercising at the same time = a building “Cycle”
(Grenadier).
• When there are more players (increased competition),
the option value of waiting is eroded. Why? Because
competitors can take your place and pre-empt you.
• If you are a monopolistic developer – there is no fear of
this! (Schwartz and Torous)
• Are property types with more competition, or locations
with more competition less prone to overbuilding, since
no player waits? (Somerville).
• The Dynamic 4-Q model assumes competitive supply
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Models be estimated empirically using real
estate data together with Economic data
• Option #1 and #2: reduced form forecast – just
evaluate and forecast rents with a model that has
either a trend or Economic Demand variables.
• Option #3: forecast rents and new supply
together. Assume market clearing. Base forecast
conditional on Economic variables.
• Option #4: add in vacancy and assume that
markets clear slowly = more variables and more
equations. Better forecasts?
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Model #1: Unconditional Univariate
(quarterly Boston data, 1979:1 - 2002:4)
R = 3.23 + .92R-1 - . 01T
•
•
•
•
(2.5) (22.1)
(.6)
R2 = .933
No trend in real office rents (.09 is not significant).
Rents depend an awful lot on last periods rents (.92)!
R* = (3.23-.01T)/ (1-.92) = $(40-.12T) (long term steady
state rents in real dollars)
How does this equation “work” when there is a lagged
dependent variable on the right hand side?
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Model #1: Implied Rental Adjustment
R = 3.23 + .92R-1 - .01T, is the same as:
R - R-1 = .08 [ (3.23 - .01T)/.08 - R-1 ]
= .08 [ R* - R-1 ]
• Rents adjust slowly (8% quarterly or about 28% annually)
to gap between: Steady state rent ($40-.12T) – current rent
• More rent lags = more zigs and zags in the adjustment
process, but around what?
• Nice and clean, but what have we learned? Trend!
• How accurate from 2002:4 through 2005:4 (Red)?
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Model #2: Conditional Univariate
(quarterly Boston data, 1979:1 - 2002:4)
R = 13.2 + .94R-1 - .06 FIRE + .023 SER
•
•
•
•
•
•
(4.1) (36.2) (3.6)
(2.9)
R2 = .942
Trend is replaced with office employment. Does that
work?
Rents still depend an awful lot on last periods rents!
Why is it that growth in FIRE jobs creates negative rent
growth? Could FIRE firms build their own space?
Why does Service grow have positive impact?
Who forecasts FIRE and SER (and how?)
Assumption: supply responds quickly
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Model #3: Conditional Multivariate:
Rent and Supply (like 4 Q)
• Suppose space Demand = 62,500 + 330FIRE + 155SER 500R-1
• Suppose Rent equates space demand to last period’s stock
(market clearing – as in 4Q diagram, dynamic model).
• Call that R* = 125+.66FIRE+.33SER - .002S-1
• But then Suppose Rents adjust gradually:
• R-R-1 = .06 [(125+.66FIRE +.33SER - .0021S-1) -R-1 ]
• Note that real rents still adjust slowly, but now to changes
in employment or stock ( 6% quarterly or 22% annually).
Also FIRE now has correct sign!
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Model #3: Conditional Rent/Construction
Multivariate (continued)
• The estimated demand side or rent equation becomes:
• R = 7.6 + .94R-1 + .04FIRE + .02SER - .00013S-1
(2.9) (17.1) (2.1)
(3.2)
(-3.9)
R2 = .976
• Also need a supply side equation:
• C = 449 + 39.7R-10 -.007S-1
(.9) (2.9)
(-2.2)
R2 = .41
• S = S-1 + C
• System will forecast rents and construction and the stock
of space – given FIRE and SER forecasts. Who forecasts
FIRE and SER? That’s what is meant by conditional.
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Model #3: Conditional Rent Multivariate
• R-R-1 = .06 [(125+.66FIRE +.33SER - .0021S-1) -R-1 ]
• For every 1000 FIRE jobs added to the economy, if we
develop 307,000 more square feet then long term steady
state real rents will be stable .
• For every 1000 Business Service jobs added to the
economy, if we develop 157,000 more square feet then
long term steady state real rents will be stable.
• Rents adjust to gap: Steady State - current rent
• If rents are at $35 real, then construction will average
about 600,000 square feet each quarter or 2.4m annually.
• FIRE growth of 7,500 jobs annually or Business service
growth of 15,000 jobs annually would justify this. But the
forecast is for each to grow about ½ of these! Hence new
supply exceeds demand, rents stagnate (Green)
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Model #4: Multivariate:
Rent, Supply and Vacancy (slow response)
• Suppose firms desired occupied stock is:
OC* = 4109 + 283FIRE + 118SER - 76R-1
• But leasing constrains tenants so that only 10% can get to
their desired stock in a period. Hence:
OC - OC-1 =
AB = .10 [(4109 -76R-1 + 283FIRE + 118SER) - OC-1 ]
(30.6) (2.1) (-3.4) (2.6)
(1.9)
R2 = .99
• V = 1.0 – OC/S
• Or rent determines vacancy.
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Model #4: Multivariate with Vacancy
• But Landlords also determine rents as a function of
vacancy. Why (bird in hand = 2 in bush)?
R = 6.5 +.81R-1 - .34V-1
(6.0) (20.1) (-6.9)
R2 = .958
So in theory, with the pair of equations, there is a rent
where vacancy is fixed (stable). And for supply:
• S = S-1 + C;
C = 1339 + 32.4R-10 -.006S-1 – 75.6V-18
(.9)
(2.5)
(-2.0) (-3.4)
R2 = .54
Now the system is complete with both vacancy and rent also
determining new supply.
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Model #4 (continued)
• Behavioral implications of slow adjustment:
• Each 1000 FIRE workers needs 283,000 square feet and
each Business Service worker 118,000. Adding this much
square feet per new worker would also keeps vacancy
(occupied square feet) constant in the long run.
• To get to these “targets”, occupied square feet responds
slowly – 10% quarterly or 35% yearly (leases).
• At current rents of $30 and vacancy of 16%, construction
will add only 50,000 square feet quarterly (.2 m annually)
• And at 16% vacancy rents will fall below $30.
• This is far less than job forecast demand is!
• Hence market goes down and eventually recovers (blue).
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Boston Office Market. Red: Univariate(#2); Green: Rentonly(#3); Blue: Rent & Vacancy(#4). Forecast from 2002:4
Actual and fitted values of real rents
60
RRENT
FOREVVAR
FORESVAR
FORE
FITTED
55
50
2007:4
45
40
35
30
25
20
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
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Boston Office Market: Rent only forecast(#3): green;
Rent & vacancy(#4): blue. Forecast from 2002:4
Multi tenant Completions
3000
CC
FOREVPER
FORESPER
2500
2000
2007:4
1500
1000
500
0
-500
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
MIT Center for Real Estate
Boston Office Market: Full model (#4).
Forecast from 2002:4
Office Absorption
3000
FOREAB
Vacancy
20
VAC
FOREVAC
2000
15
%
1000
10
0
-1000
5
2007:4
-2000
0
-3000
1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010
1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010
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Additional criteria for evaluating models: “back testing”
This is a forecast for Boston house prices using a Univariate model
(#2). Why does this model work well here – but not for office?
Boston
Boston
2006:4
2006:4
1998:4
983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
Forecast from 2006
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Forecast from 1998
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Forecasting Lessons
• When supply adjusts quickly to prices or rents, then
little is to be gained from a model that jointly
forecasts the two – Just use a Univariate model (#2)
(Single Family Housing )
• The slower supply responds and the more gradual
prices and rents adjust, then the more you need to
forecast both sides of the market (#3 or #4) to
capture its momentum and cyclic swings.
(Apartments, Office. Hotels, Retail)
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Distribution of Forecast Outcomes
• A “forecast” is the mean value of the variable(s)
being forecast. Any forecast has a probability
distribution surrounding it.
• The further out you go the wider is the forecast
probability distribution of possible outcomes. Why?
• Variables that are “random walks” are forecast with
simulations – wherein the starting value plays no
role. With mean reversion, real estate forecasts
obviously depend on where the market currently is.
Historic volatility not enough to estimate “risk”.
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What is Risk: Historic Variability vs.
Forecast Uncertainty
(Notice true risk grows over time)
Historic
NOI
Variability
Forecast
Uncertainty
TIME
MIT Center for Real Estate
Forecasts give “standard errors” which can be
used to generate confidence bands or the
probability distribution of future income
Collateral Income
$250,000
$200,000
$150,000
$100,000
$50,000
$0
0
1
2
3
4
5
Years
6
7
8
9
10
MIT Center for Real Estate
What determines confidence band width?
• A market with wide historic swings will tend to
generate wider confidence bands in the future –
unless you can “explain” these swings accurately.
• A “poor” model (low fit) means you do not
understand the forces affecting the market. What you
don’t know = risk.
• Low quality data, missing observations, a short
historic data series, no variables that capture what
really drives the market = a “poor” model.
MIT Center for Real Estate
Atlanta Office Risk Calculated along probability “paths”
Yield = 7.1%, expected IRR = 7.3% (base case)
Std Deviation of IRR = 5.0
Standard
Errors
Away from
Base Case
Average
NOI
Growth
2002-2007
Average
Appreciation
2002-2007
IRR
2002-2007
-4
-3
-2
-1
0
1
2
3
4
-16.6%
-11.3%
-6.6%
-2.3%
1.6%
5.2%
8.6%
11.9%
14.0%
-20.9%
-15.1%
-9.7%
-4.8%
-0.2%
4.2%
8.4%
12.4%
15.4%
-15.1%
-8.7%
-3.0%
2.3%
7.3%
12.0%
16.6%
21.0%
24.4%
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Is “forward risk” reflected in pricing?
Industrial markets: 2005-2012
y = -0.6016x + 10.134
R 2 = 0.0331
Ra w Re g re ssio n
18
16
Expected Return
14
12
10
8
6
4
0.0
0.5
1.0
1.5
2.0
Ris k
2.5
3.0
3.5
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Confidence Bands can be compared to the Loan
Obligations of a Commercial Mortgage
Collateral Income
$250,000
$200,000
$150,000
$100,000
$50,000
$0
0
1
2
3
4
5
6
7
8
9
Years
+/- 2 Standard Deviations
+/- 1 Standard Deviation
Baseline Income
Debt Service
10
MIT Center for Real Estate
The probability of Default is then the probability that your
forecast predicts insufficient NOI to cover Debt service!
Debt Service
Delinquent Probability
Full Payment Probability
Shortfall:
Loss Given Default
-2 SD
-1 SD
Base
+1 SD
NOI Probability Distribution
+2 SD
MIT Center for Real Estate
Debt Risk Metrics
• PD: Conditional (to getting there) Probability of
Default. Area in the NOI probability distribution
that represents outcomes< debt service.
• Loss at each outcome = Debt service - NOI
• Expected Loss: ∑ probability of outcome x
outcomes loss at that outcome
• Severity (Loss Given Default, LGD) = EL/PD
• Value-at-Risk: Loss (e.g. Loan Balance – value)
associated with a particular point in the probability
distribution (e.g. 95% confidence = 5% worst
outcome).
MIT Center for Real Estate
Debt Risk Metrics (continued)
• What about time? There are 10 years in which
loan can default.
• Dt: Unconditional likelihood of Default at time t.
The likelihood that the loan defaults and that the
default occurs in year t.
• Dt = St-1 x PDt .
• St = St-1 x (1- PDt), S0 = 1. (recursive equations)
• “Hazard” function: a competing risk over time.
• Lifetime Default = ∑ Dt
• The Basel Agreement is coming!
MIT Center for Real Estate
Forecast Risk Metrics:
The next wave of Risk Management (and Basel
Compliance)
Risk measures over 3-year term
Future default
frequency
Loss given
default
Expected loss
Unexpected
loss at 95%
Rating
Yeld
degradation
0.98%
$ 100,295
$ 983
$0
2
10
Risk measures by year
Year
Default
frequency
1
0.86%
$ 98,934
$ 851
$0
2
3
4
5
6
7
8
9
10
0.77%
1.29%
1.43%
1.24%
0.94%
0.44%
0.35%
0.32%
0.20%
$ 97,794
$ 102,695
$ 120,935
$ 149,784
$ 138,057
$ 112,738
$ 104,450
$ 89,791
$ 85,301
$ 753
$ 1,325
$ 1,729
$ 1,857
$ 1,298
$ 496
$ 366
$ 287
$ 171
$0
$0
$0
$ 92,547
$ 93,931
$ 94,043
$ 94,145
$ 94,239
$ 94,327
Loss given default
Expected loss
Unexpected loss
at 95%
Figure by MIT OpenCourseWare.
MIT Center for Real Estate
Application: Future Annual Default and loss expected to
be small Compared to history (and current CMBX)!
Default Rates
(60+ Days)
8%
History and TWR Forecast
Loss Rates
(Charge-off)
2.00%
7%
1.75%
6%
1.50%
CMBX Implied
5%
1.25%
1.00%
3%
0.75%
2%
0.50%
1%
0.25%
0%
0.00%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
4%
Com m ercial Banks' Charge-off/Loss Rate (R)
2007 Vintage (R)
CMBS Universe (R)
ACLI Default Rate (L)
CMBS Default Rate (L)
Sources: ACLI, FDIC, Trepp , Moody’s, CBRE Torto Wheaton Research