100 years of commercial real estate prices in Manhattan

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Paper

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100

YEARS

OF

COMMERCIAL

REAL

ESTATE

PRICES

IN

MANHATTAN

William

C.

Wlieaton

Mark

S.

Baranski

Cesarina

A.

Templeton

Working

Paper

08-02

December

10,

2007

Room

E52-251

50

Memorial

Drive

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MA

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42

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107535

HB31

Draft:December

10, 2007.

100 years

of

Commercial

Real

Estate

prices

in

Manhattan

William

C,

Wheaton

Department

of

Economics

CenterforRealEstate

E52-252B,

MIT

Cambridge, Mass. 02139

[email protected]

Mark

S. Baranski

Overland

Development

Group,

LLC

Boston,

Mass.

CesarinaA.

Templeton

Overland

Realty Capital,

LLC

Boston,

Mass.

ABSTRACT

This paperis abletoputtogether a database of

86

repeatsalestransactions for office

properties inlower

and

mid town

Manhattan

spanning theyears

from 1899

through 1999.

Using

thisvery limited data base, decade-interval changesinreal property pricesare

estimated- with varying degrees ofprecision.

Our

conclusionsaretwo.First, adjusting

for inflation,

commercial

officepropertyvalues are

30%

lowerin

1999

than they

were

in

1899. Secondly,within

any

decadevalues oftenrise

and

fall

by 20-50%

in realtemis.

With

theseresults, thelong termhistoric returnto

New

York

commercial

property

must

be

mostly be

comprised

ofyieldwith capitalgains limitedtogeneral inflation. Other

I. Introduction.

Recentlytherehas

been

renewed

interestthe longtermappreciation

of

real estate assets

-both thoseoccupied

by

households aswellthatused

by

firms. Earlystudies, suchas that

by Hoyt

[1933]

and

laterMills [1969] focused

on

land values

and

showed

thatwhile the

aggregate assessed value

of urban

landsoars during periods

of

rapid

urban

growth, land

value per capitain

Chicago

was

almostthe

same

in

1930

as in 1

845

-

when

adjusted for

inflation. This is consistentwith therecent

work

of

Atack

and

Margo

[1998].

Using

actual landparcel transactions

from

New

York

Citythey determinethat

any

pricelevel

increases firom

1835

to

1900

are

mainly

due

tothe Civil

War

decade's general price

inflation. Factoringthat out, Manliattanland prices in

1900

were

quite similarinreal dollars to 1835. Studiesofland valuesinthe

20*

century,suchas that

by

Edel

and

Sclar

[1975] again

showed

little gaininassessed residentialprices

beyond

inflation in

Boston

overthe period

from 1900

to 1970.

A

1997

paper

by

Eicholtz

however,

has caused

many

researcherstopause. Consistentwiththe

work

justdiscussed Echoltz

shows

thatthere has

been

littlereal

growth

in

Amsterdam

house

prices since

1628

-

almostthreecenturies.

Despite

no

longtermtrend,therearesustained swings in realpricesoverparticular

periodsranging

from

asshortas a

decade

to aslong as 50 years.

Studies

of

commercial

property values (e.g. Fisher,Geltner,

Webb

[1994]) all arebased

on

more

contemporary

data

and have

tended also to findlittleappreciation

beyond

inflation

-

atleastsince the late 1970s. Similarly, several authors

have

notedthat

commercial

propertyrents tendto

be

stationary inrealdollars (e.g.

Wheaton

and

Torto

examining

100years of

commercial

office values in

Manhattan

-

creatingarepeatsales

priceindex withactual transactionsdata.

We

findthatsince 1

899

ouroffice index

suggests values inManliattan

have

actually fallen slightly

-

adjustedfor inflation.

We

alsofindthatduring

many

individual decades, prices

have

risen orfallenas

much

as

50%

on

top ofinflation

-

butthesedecadal estimates arevery imprecise.

We

devisea test for

whether

the cumulative(real)appreciationoverthisperiodis significant

from

zero

-

and

ourfindings areupheldat

wide

confidencelevels.

Thus

the long

term

returnto

owning

commercial

realestatein

New

York

is

composed

mostly ofyield plus appreciationthat

equalsonlyinflation.

We

review

some

other studies ofreal estateinvestmentreturnsthat

suggestthe yield

from

real estate historically

may

in fact

have

been

high

enough

to cover

boththe considerablerisk

and

lower appreciationthatisestimated inour

work

here.

Our

paper

is organizedas follows. In thenextsection

we

briefly

review

some

theoretical

arguments

and

empirical factsabout long term propertyvalues

and

assetpricing

-

to try

and

reconcile thefindings todate. SectionIIIthen describesour efforts tocollect

repeatedsales transactions for a setof

Manhattan

officebuildings. Section

IV

reviews our

estimation

methodology and

section

V

presentsourresults. In Section

VI

we

illustrate

some

corroborating evidence,while inSection

VII

we

draw

some

concluding

observations.

II. Theoretical

Models

of

Real

EstateAsset Prices.

Realestate assetpricetheory starts withthe Ricardian

Monocentric

rent

model

(e.g.

Muth

income)

acrossurbanlocations.

The

comparativestaticresultsofthis

model

(as

shown

by

Wheaton

[1974]) are

unambiguous.

Population

growth

aloneis sufficient to generate

significant realincreasesinland orhousing rent

-

iftransportationtechnologyremains

the same.

The

problem

isthatoverthelast

two hundred

yearsthishas not

been

the case.

Infact relative to

walking

(thepreferred

mode

in 1800) early streetcars,

subway

rail

transit

and

then automobiles

have

allincreased the speedoftravel

by

afactorofas

much

as 10.

Even

allowingforthe greater

monetary

costs ofthese

newer

modes

oftravel,it

could easilybe arguedthat such

improvements have

offsetthe impactof population

growth

-

leadingto theempiricalresults ofEicholtz

and

others.

While

population

growth

and

spatial expansion shiftthe rentgradient

upward,

transportation

improvements

"flatten" it

from

the

edge

inward. Virtuallyallstudiesof long term changes in land

gradients, suchas Mills, or

Atack and

Margo

find strong evidenceof"flattening"

-

in

most

cases sufficient togenerate little

change

in average overallvalues.

Gin and

Sonstelie

[1992] demonstrate

how

specifictransportation

improvements

(the

development

ofthe

streetcar)are directlyresponsibleforthe decliningland rent gradientinthe latterhalfof

the 19"" _century.

Muth's

version ofthe monocentric

model

alsopoints outthat instudying

urban

history,

we

must

considerthe role

and

costofhousing capital aswellasthe elasticity of

substitution

between

capital

and

land.

We

know

of only

one

systematic study

on

longer

term changes in

US

construction costs

(Wheaton

[2006]}

and

this

shows

thatthey

have

grown

justwithinflation

-

atleastsince 1967.If thisholdsaswellover longer spans of

Added

totheevidence

on

landrent

-

it

would seem

thatboth "factors of production"

might

not

grow

fasterthaninflation. Inshort, giventhehistoric changes intheparameters

ofamonocentricmodel, itisnotalall clearthat

we

should expectthe annualrentforreal

estate to

have

grown

fasterthaninflationoverthelastcentury ortwo.

In aseminalpaper,

Capozza

and

Helsley [1990] comiect urban rent

models

with

modern

assetpricing theory.

These

authorsallow populationortravel costs to

change

over time

withuncertainty

and

this then generates landrents that

grow

and

fluctuateaccordingly.

Capozza

and

Helsley then deriveassetpricesfor landateachlocation

and

the

option-basedrule fordeveloping

new

landatthe urbanedge.

The

ratio ofassetprices tocurrent

land rent varies across location exactly asFinance

Theory

would

suggest

-

being higher

atlocations

where

rentis

growing

faster

and

lower atlocations

where

there is

more

uncertaintyaboutthisgrowth.

Recently, themonocentric

model

itselfhas

been

modified.

Ogawa

and

Fujita [1980],

Anas

[1996],

and

Wlieaton [2005] all arguethat as citiesgrow, the resultantincrease in

congestion costsgenerates incentives forjobs todisperse.

Such

jobdispersal leadsto

shorter

commuting

and

toland rent gradientsthat

may

have

little or

no

slope.

Hence

even

withconstant transporttechnologyurban population

growth

may

leadtolittle increasein

landrent.

McMillen

and Smith

[2003] findempirical supportforthe

argument

that as

economic growth

beginsto congest the transportationnetwork, firms disperse spatially

and

in thatprocess thisdispersal significantlyameliorates the congestioninduced

by

Inshort, thejuryis clearly stillout

on whether

we

should expectland orreal estate rentto

grow

above and

beyond

inflation. Historically,

major

technological advances in

transportation construction costs

may

have been

sufficient to offsetrapid

urban

growth.

Going

forward, thereis theprospectthat

employment

dispersal

may

do likewise.

Whatever

rent

grov^h

patterstechnologydictates shouldbe capitalizedintovalueswith

appropriaterisk

premiums.

III. Collectingtransaction datafor

Manhattan

OfficeProperty.

We

began

thepresent study withan inventory ofcurrently standing officebuildings in

Manhattan

courtesy

of

theCostarGroup,

and

Torto

Wheaton

Research. Inboth ofthese

databasesthere isinformation only

on

building age, stories

and

square feet

-

nothing

on

"quality", "prestige"orarchitecturalvalue.

We

restricted ourselvesto institutional grade

properties, 10 or

more

stories, withelevators

and

whose

total square feetis at least

250,000. This initial filterruled out

most

ofthetruly older properties in Manliattan(built

priorto 1880).

The

resulting

sample

contained

253

propertiesin

"Midtown"

Manhattan

and

82

"Downtown"

office buildings.

The

Costar databasecontainsan estimate ofthe datethatthe building

was

originally

constmcted.

These

dates

were

mostlyclustered in

two

distinctperiods

1890-1929 and

1960-1989. For each propertythebuilding address

was

matched

withthe building data

baseinthe

New

York

CityConstruction

Record

Guide and

the

New

York

City Building

Records

Office.

These

databasescontainall original construction

documents and

and

we

were

able toobtain a firm estimateofalloriginal tenderedconstruction costs for

the building.

These

costs

were

inthe range

of

$10-$20

per squarefoot forthefirst cluster

of

properties(1910-1929)

and S60-$240

forthe

more

recent period.

To

determine"total"

development

costs,

we

useda conservative "rule of

thumb"

inthe industrythatland

and

softcosts constitute slightly

more

thanhalfofthe total

development

ofa property.

Thus

increasing the original construction costs

by

afactorof1.2provided anestimate ofthe

property's initial"value"-at the time of development.

With

this inifial"transacfion",

we

thensearchedthe sales

and

transfers containedin the

database ofthe

New

York

City Real Estate Board. Thisdatabaseis organized

by

address

and

contains a

huge

number

of

"exchanges"

and

"transfersofinterest"(total orpartial) in

additionto full titletransfers. For each transaction, in additionto a date, there isa dollar

valuebased

on

the transfer taxrate that

was

thenineffect.

These

dollarvalues

were

then

inflated into

1999

(constantdollar)values. Inordertobe surethat

we

were examining

true

"arms

length" transactions,

we

restricted ourdefinitionofa"sale"toinclude only

transfers that

met

allofthe followingcriteria.

-

The

buyer

and

seller

had

differentlast

names

or

were

differententities.

-

Bank

orother"foreclosures"

were

excluded.

-

A

fiallpropertytitle

was

transferredwith

no

residual claims orpartial interests.

Another

consideration is

whether

theproperty

was

significantly alteredorrenovated.

Of

course overthis timespan,

many

properties builtinthe earlyperiods

would

have

had

property redevelopments.

Such

changes areoftennotedinthe Costardatabase (along

with adate).

Hence

any

pairoftransactions that

spanned

a Costar"redeveloped"date

was

excluded.

With

all ofthese variousfilters,the finaldatabase contained only 86

transactionpairs.

32

pairsoccurredin 17

downtown

properties

and 54

pairs

were

observed in

28

midtown

properties. Several properties

had numerous

"sales". Table 1

contains a hstofthe

45

properties

and

theirsale dates.

[Table 1]

As

afinal filter

we

removed

5 observationsin

which

properties

changed

hands within2

-years atpricesthat

were

more

thantwiceorless than halfofthatatthefirst date.

Most

reported indicesarethus constructedwith 81 sale pairs.

IV,

Survivorship

and

otherBiases.

The

procedure

used

to createthe repeatsales does

have

the possibilityof aninteresting

bias to it

-

itprecludes selectingpropertiesthat

have

not survived.

There

is a long

literature

on

survivor bias inthe analysis

of

stocks

and

mutualfiinds (e.g. Elton, Giubber,

Blake

[1996]), butinthe caseofreal estateproperties,

we

show

thebias (a)tendsto

be

very,very small

and

(b) can runin either

of

two

directions

-

hence

possiblynone.

The

company

Emporis

(emporis.com) maintains ahistoricalbuilding inventory forthe

major

cifiesoftheworld. In

New

York

they listauniverse of "highrise"buildings

which

contains

5579

properties (ofall uses).

"High

rise" isdefinedslightly

more

broadly than ourfilter:

10+

stories, butwithout a

minimum

size.

A

little

more

than

4000

ofthe listed

demolished.

Only

178 "highrise"propertiesbuilt since

1899 have

ever

been

demolished.

Hence

any

survivorship bias issimply of

no

consequence. Surviving "highrise"

propertiesrepresent

more

than

96%

of

allsuchproperties ever constructed.

The

investment

performance

of

a

sample

of suchproperties will

be

virtuallyidentical to that

ofthepropertyuniverse atlarge.

In addition, asdiscussed in

Wheaton

[1982], there are

two

conditions

under

which urban

re-development can occur.First, buildings are

more

likely tobe

demolished

and

replaced (and

hence

not

show

up

inthe sample)

when

thelandunderneath

them becomes

more

valuable overtime. This could

mean

thatsurviving properties

had

lower land value growth. Second,properties are also

more

likely to

be demolished

when

theircapitalhas

become outmoded

or depreciated. This

would

mean

thatsurviving properties

had

increasedcapitalvaluerelative to non-surviving.

On

netthen itisjustimpossibleto say

which

way

theverytiny survivorship bias operates

-

ifit existsatall.

Finally, allrepeatsaleindexes suffer

from

a

more

troublingset of problems, recently

researched

by

Harding, Rosenthal,

Sirmans

(2007). Ifproperties deteriorate over time

intrinsicallywithage,then the indiceswillunderestimate"true"price appreciation. If

improvements and

renovationsare

made

totheproperty

between

saledates then the

approach

overestimates "true" price appreciation.

The

bestthat

we

could

do

was

to drop

those properties

from

our

sample

that

were

listed

by

Costar ashaving

undergone

IV,

Estimating

Decade

Inflation rates

with

a

Repeat

Sales

Model.

Itisclearthatwiththe limited

number

ofclean transaction pairs (86 or 81)

we

would

not

be ableto

measure

price appreciationwith

much

precision. Yearlyappreciationrates

would

be impossible,

and

using longintervalsruns theriskofviolating the assumption

thatappreciationwithin the interval is constant.

As

a

compromise

we

decidedtouse

decades

-

which

would

involve 10 degrees of freedom.

The

approach

works

as follows.

Following Bailey,

Muth

and

Nourse, considerthe

model

of propertypricing(P) in

equation (1).

The

vectors

X

and

B

representproperty attributes

and

"Hedonic"

coefficients therefore.

Then

there are"fixed effect"variables foreach

decade

Dj, along with correspondingcoefficients Oj.

We

observetheproperty first during decadeT',

and

we

define Sjt' as equalto 1 ifdecadej isprior tothe transaction

decade

T'

and

equal to

the fraction

of

decadeT' thathas passed beforethe actual observedpricedate

when

j=T'.

When

j> T'

we

set Sjt- equal to zero. Inthis

model

ratherthan

have

a single fixedeffect

fortheyear(or in this case decade) thatthe propertyis observed

we

have

the

sum

ofthe

yearlyeffects (decades)leading

up

totheobserved year

from

some

base year 0=1)-

Thus

theestimatedvalues ofaj representthatdecade's inferredpriceappreciationraterather

than itspricelevel.

Z',,

=^'Zexp(X«,^,^,r)

₍₁₎

Ifthis

same

property thensells at alatertime period (T>T')

we

have

a

new

set

P,

=^'A^exp(^«^Z)^.5,,)

(2)

Taking

theratio

and

thenlogs

we

get:

log(P,)

-

log(P,,)

=

«,,Z),,(1

-

S,.,.)

+ Y.ajD^Sj,

₍₃₎

j=r+\

Out

particularapplicationofrepeatsales

methodology

has aunique shortcoming (in

additionto allthe

normal

criticisms as discussed

by Goetzman, Case

and

PoUakowski

[1992]).

As

createditis based

on

the assumptionthatpriceappreciation isrelatively

uniform

duringthe intervalsrepresented

by

the fixed effects. In

models

with quarterly or

yearly fixedeffects, thisassumption is probably nottoo faroff, butwith decadeintervals

itis clearlyabit

of

a stretch. Unfortunatelythatisallthatispossible with ourlimited

sample

size.

V. Results.

The

primary equation estimateduses 81 sale-pairs ofobservations

and

includes

no

other

datathanthe 10 decadal

dummy

variables. This is

shown

in Table2. Itis clearthatthere

isvery littleprecisiontothe estimates

of

appreciation

from 1899

through1929.

During

the depression,

WWII

years

and

the early post

war

boom

the estimates

have

abitof

precision, butstandarderrorsarestill almostas large asthe coefficientitself It is only the

effects forthe lastthree decades thataretruly statistically significant. It

must

be

remembered

thatallestimates areof decadeappreciationinrealterms

-

after

CPI

InTable 3

we

explorethe issue of

whether

midtown

and

downtown

Manhattan might

have

had

systematicallydifferent overall (100year) appreciation.

To

do

thiswith

two

sets

of

dummy

variables

would

clearly stretchthe

sample

-

particularly

downtown

where

there

were

only32 observations. Instead,

we

constructeda variable

which

was

the

productofa

midtown

(location) fixedeffect

and

the

#

yearsthat

spanned

eachsalepair

(thevariable

MMID

inTable 3).

We

interpretthe coefficient

of

this variable as the

average 100 year annual differencein appreciation

between

midtown -

relative to

downtown.

Its significancesuggeststhat yearly appreciation

was

on

average slightly less

than apercentper yeargreaterin

midtown

than

downtown -

overthe lastcentury.

[Tables2, 3]

InFigure 1

we

takethe estimatedcoefficients inthe base

model

(Table2)

and

reconstruct

from

equation(1) an index ofPricelevels.

From

1899

to 1919,real prices

decHne

a little

lessthan

1%

yearly.

Then

during the 1920sthey rosealmost

3%

yearlyin real terms.

The

depression

saw

realpricesdrop in half, aiadthe 1940s

saw them

slightly

more

thanfully

recover. Real prices

dropped

about

2%

yearly

from 1949

to

1969

and

thenrose

3%

yearly

from 1969

tothe

famous

peak

inpropertyvalues of1989.

From

1989

to

1999

prices

dropped

in

half-

againadjusted for inflation.

Our

datadoesnotcoverthe widely

heraldedresurgencein

New

York

prices ofthe last7-8 years.

The

conclusions

from

Figure 1 arequite clear. Just likehousesin theEicholtz study,

Manhattan

officespace did notoutpaceinflationoverthe previouscentury. Similarly,

over

any

given 10-30 year periodprices canriseorfallquite considerably in realterms.

Real estatethenhas lots ofriskover reasonableinvestorhorizons.

The

imprecision with

which most

decade inflation ratesare estimatedis a cause of

concern.

However,

itis actuallypossible toconstruct a

much

more

precisetestforthe

cumulative

change

inthe price index overthe 10 decadalintervals. In

some

sensethis is

the questionof

most

interest.

The

test for

whether

the

sum

ofthe decadaleffectsis

different

from

zero is distributed"F",althoughitsvalueinvolvesa complicated

calculationusingthe full

VCV

mati-ix ofthe individualdecadeeffects.

We

findthatthe

F

value forthe

models

inTables 2-3 ranges

from

.51 to .96.

With

theappropriate degrees of freedom, the nullhypothesis of

no

cumulative

change

inrealpricelevels canbe rejected

onlyata .53 to .67 confidencelevel

-

nowhere

near

normal

test limits.

Hence

while

we

are

somwhat

unsure about each decade'sinflation,

we

are quite firm

ground

asserting that

overthis century cumulativerealprice

growth

was

not significantly different

from

zero.

VI.

Corroborating Evidence:

the

Return

to

Real

Estate.

The

conclusionthat inthe lastcentury,

New

York

real estatehasnotoutpaced inflation in

terms ofappreciation is fully supportedin

two

othersourcesofdata.

The

first is direct

data

from

thelong-term

BLS

surveyof urban apartmentrents

-

asurveyconducted since

basis. Currentlyalmost 80

MSA

arepartofthe survey,

which

was

startedjust after

WWI.

Originallyonlyahandful ofcities

were

surveyed

-

including

New

York.

The

BLS

survey is a repeatsample.

Each

period, units areresurveyedtoasses

any change

inthe rentthatthe currenttenantispaying.

When

tenants change,the landlordis

contacted,

and

the

new

rent(andtenant) isobtainedfor futuresurveying.

Recent

work

hascriticizedthe construction

of

suchrepeat-surveyhousing indexes.

Nakurma

(2007)

argues thatthe

BLS

misses

many

rent increases thatoccurastenants change,

and

that in

additionthey failto correctforthe inJierent

downward

biasthat exists in suchindices

due

to depreciation.

As

discussedpreviously, there isalsoconsiderable

upward

biasinrepeat

transaction indices

due

to

improvements and

actual

maintenance

expenses.

The

debate

on

whether

these

two

long

mn

biases cancel outis stillopen.

InFigure2

we

present the

CPI

rent series fortliree cities since 1918, including

New

York.

The

indexes aredeflatedto constantdollars to

be

consistentwith Figure 1.

The

conclusionis thatrents are

no

higherin

1999

than 1918

when

adjusted for inflation.

Some

ofthe cyclic

movements

inFigure2 are also consistentwiththe findingsdisplayed

inFigure 1.

Between

1918

and 1930

bothmarkets experiencesignificant real

appreciation,

and

between 1930 and

the early 1940s both

show

significant real

depreciation.

Both have

increases following

WWII,

and

there is

common

realprice

growth

duringthe

boom

ofthe 1980s.

What

emerges

from

both ourstudy

and

the

Govermnent's

CPI

datais that realestate is an

asset

whose

income and

value

keep

pace with inflationoverthelongrun.

At

the

same

time, itexperiences considerableriskat

decade

orhigherfrequency.

An

important

questionthenis

whether

theyield

from

realestateprovides areasonablereturnto

investors. Forthis to

be

the casereal estateyields

must be

equivalenttothe

market

risk-freereal return plus a

commensurate

risk

premium.

The

realinterest rate

on

treasuries

(ex post)tendsinthe long runto be closetothereal rate of

economic growth (2-5%) and

the

Moody's

risk

premium

for

BAA

bonds

has ranged

from

1%

to

3%

since

WWII. As

discussed

by

Blanchard

(1993), the equityrisk

premium

has

been

much

higher

historically, although ithasdeclined shaiplyinrecentdecades.

Thus

ifrealestate

appreciateswithinflation

we

might

expectyieldsto

be

inthe

5%-10%

range.

A

second

supportingstudyis

by

Kaiser(1997),

who

creates along termseries

on

the

total investmentreturn

from

officebuildings.

From

1977

forward,the study uses well

known

NCREIF

nationaloffice data

-

which

inFigure 3 has

been

updated through 2006.

From

1926

to 1977, Kaiserdevelopsatotalreturnseries

from

private portfohos

and

prior

studies

of urban

officebuildings.

From

1977

to

2006

the

NCREIF

databreaksouttotal

return intoappreciation versus yield,

and

duringthistime

much

ofthe returnhas

been

yieldwith appreciationin factbarelykeeping

up

withinflation.

We

have been

quite

successfulat

modeling

the share oftotal returnthat isyield

-

overthe

NCREIF

period

-as afunction ofinterestrates

and

office

market

vacancy.

During

periodsof high vacancy,

appreciation isnegative

and

yieldstendtorise (and vice-versa).

We

applythis

model

vacancy

inthelargest

US

cities. Thisproducesthe estimatedofficeyield series (forprior

to 1977)thatis

shown

inFigure3.

Figure3 reveal

two

features aboutoffice investmentreturns. First,yieldstendtobe

stable,

and

well

above

risk free real interestrates.

From

1940

to

1980

for example,the

estimatedyieldsrange

between

8%-9%

whilereal treasuriesaveraged

2%-3%

(excepting

theperiodof highinflationinthe late 1970s).

Such

yields

would seem

toprovidean

ample

risk

premium

(500bps+). Secondly,the appreciation

component

(thedifference

between

totalreturn

and

yield)cumulatively aggregates

up

tobeingslightly lessthan

CPI

inflation. Appreciationis also

more

volatile

and

itstiming is similartothatof Figure 2:

the

two major

episodes

of

price deflation occurinthe 1930s

and

thenearly 1990s inboth

series.

[Figure 3]

VII.

Why

does

Real

estatenot appreciate

more?

The

results ofthis analysis arecompletely consistentwitha

number

of

stylizedfacts. For

example

itis widely

known

thatthe

Empire

statebuilding

was

constnictedfor about

$22

asquarefoot

from between

1928-1930. Itis also

acknowledged

thatthe 1920's

saw

rampant

land inflation(ourdata

show

this aswell)

and

so

we

might

boost our estimate of

thenon-construction share

from

1.2 tosay 1.5 times constructioncosts. Thisgivestotal

between

1929

and

2000 and

one arrives at

an

estimateforcurrent priceof about

$500

per

square foot.This is reasonably closetotransactions prices inManliattan inthe late 1990s

for

prime

properties.

The

results canalsobe consistentwiththecombination ofhistoricalpopulation

growth

and

transportation

improvements

thatcharacterized

New

York

sincethe early 1800s.

From

1830

to 1900,

New

York

City

grew

from

a populationof

300

thousandto 1.8

million

and began

to spill

beyond

Manhattan.

With

atriplingof average density, such

growth

would

have

necessitated adoubling ofthe city'sradius

-

orequivalently of

average

commuting

distances. Itis easyto

imagine

thatthe introductionof

even

the

inefficientstreetcar

doubled

average

commuting

speedleavingtotal

commute

timesto

the

urban

"edge"thesame.

From

1899

to 1999,the City'spopulation

grew

roughly four

fold again,

and

expanded

intothe fulltri-state area.

During

thisperioddensity actually

began

to decline.

Even

ifthedistance to

New

York's urban

edge

had

increased four fold

-the

commensurate

greater speedsof

underground

subways, trains

and

automobiles

could stillleave average

commute

timesto the

edge

constant

-

and hence

realland values

aswell! Ifconstruction costs

grew

only withgeneralinflationduringthis century

-

as

they

have

since the 1960s

-

thenassetprices

would

alsonot increasein realterms.

Going

foi-wardthe

enormous improvements

intransportationthatcharacterized thelast centuryarejustnotapparent. Fortunately,

New

York's

populationis expectedto

grow

only very slowlyif atall

-

nothing like the

6%

yearlyratesofthe 1800s

-

or

4%

rates of

tobe theincreased suburbanizationofjobs. Sliilton, lias

shown

that in

most major

US

metropolitanareas, corporateheadquarters

have

completely

moved

to suburban

"edge

cities".

As

aresult,metropolitanareas are

becoming

more

and

more

"polycentric"

(Guilliano

and

Small). Inmetropolitanareaswith dispersed

employment,

population

growth

is

accommodated

not

by

longer

commutes

and

risingdensity,but

by

thecreation

of

more

and

newer

"edge

cities" (Helseley

and

Sullivan). If thisprocess continues, then

even

in faster

growing

metropolitanareas, average

commuting

times

and

landvalues

need

notrise inthe future.

Thus

History

may

in factrepeat itself-albeit for different

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DispersalinCitieswith

Table

1:

Property Transactions

1466Broadway 1907, 1997 730FifthAve. 1921, 1939, 1946, 1948, 1966, 1991, 1999 535Fifth Ave. 1925,1963,1984 220East42"''_{St 1922, 1982} 275MadisonAve 1950,1952, 1965, 1980, 1984, 1988 1450Broadway 1939, 1946, 1964, 1988, 1999 500FifthAve. 1938, 1988, 1996 640FifthAve. 1941, 1961, 1964, 1989, 1997 1740Broadway 1950, 1990 1120Ave.Americas 1964, 1978 150 East42""St. 1951,1955,1987 530FifthAve. 1952, 1978, 1994 666FifthAve. 1953,1977, 1987 717FifthAve. 1952,1978, 1993 1285Ave.Americas 1960,1989 685 3'''_Ave. 1989, 1993 1180 Ave.Americas 1968, 1995 1301 Ave.Americas 1967, 1988 6East43^' St. 1962,1994 1250Broadway 1962, 1999 150 East58'" St. 1962, 1983, 1998 1500Broadway 1971, 1979, 1996 10 East53'"St. 1970, 1975, 1982, 1993 6003'"_Avenue 1970, 1977 1211 Ave.Americas 1970, 1978, 1999 825 8'"_{Avenue 1983, 1998}

750Lexington Ave. 1984, 1997 1177 Ave.Americas 1988, 1991 100Broadway 1968, 1981 37WallSt. 1904, 1956, 1968, 1984 90WestSt. 1905, 1981, 1984 115Broadway 1907, 1960, 1986, 1988, 1994, 1997, 1999 14WallSt. 1912, 1987, 1999 233 Broadway 1903, 1998 61 Broadway 1916,1973,1988,1997 25Broadway 1928, 1962 110WilliamSt. 1952, 1970, 1981 222Broadway 1968,1984,1988,1997 59Maiden Lane 1965,1981, 1999 140Broadway 1966, 1998 95WallSt 1968,1999 100WallSt. 1969,1997 100GoldSt. 1970,1983 100WilliamSt 1977,1999 40Broad St. 1987, 1998

Table

2:

Base Equation

Usable Observations 81 Degrees ofFreedom 71 TotalObservations 86 Skipped/Missing 5

CenteredR**2 0.308698

RBar**2

0.221068

UncenteredR**2 0.311463

TxR**2

25.228

Mean

ofDependentVariable 0.0326646141

StdErrorofDependentVariable0.518693711

6

StandardErrorof Estimate 0.4577840420

Durbin-Watson Statistic 1.616997

Variable Coeff Std Error T-Stat Signif 1. Dl 2.

D2

3.

D3

4.

D4

5.

D5

6.

D6

7.

D7

8.

D8

9.

D9

10.

DIG

-0.239671368 -0.177990872 0.415138235 -0.749460348 0.849830584 -0.270387407 -0.254682758 0.289917011 0.496816493 -0.751664458 0.887744850 0.620131228 0.528407052 0.610548281 0.553189217 0,250403102 0.197519310 0.155111093 0.178714342 0.183741205 -0.26998 -0.28702 0.78564 -1.22752 1.53624 -1.07981 -1.28941 1.86909 2.77995 -4.09089 0.78796053 0.77493200 0.43469188 0.22368150 0.12892457 0.28388195 0.20144141 0.06573457 0.00695438 0.00011198

Table

3:

Separate

Midtown

trend

Usable Observations 81 Degrees ofFreedom 70

TotalObservations 86 Skipped/Missing 5

CenteredR**2 0.356249

RBar**2

0.264284

UncenteredR**2 0.358823

TxR**2

29.065

Mean

ofDependentVariable 0.0326646141

Std ErrorofDependentVariable0.518693711

6

StandardErrorof Estimate 0.4449036891

Durbin-WatsonStatistic 1.720412

Variable Coeff Std Error T-Stat Signif 1. Dl 2.

D2

3.

D3

4.

D4

5.

D5

6.

D6

7.

D7

8.

D8

9.

D9

10.

DIO

11.

MMID

-0.023833913 -0.266088265 0.797246294 -1.181635802 1.019181852 -0.425252459 -0.317602494 0.252726674 0.390248732 -0.774134415 0.009706928 0.867972776 0.603927035 0.540334296 0.623065561 0.542758608 0.252708125 0.193945907 0.151631495 0.179897872 0.178844615 0.004268882 -0.02746 -0.44060 1.47547 -1.89649 1.87778 -1.68278 -1.63758 1.66672 2.16928 -4.32853 2.27388 0.97817155 0.66086266 0.14457068 0.06202271 0.06457728 0.09687309 0.10599674 0.10003958 0.03345952 0.00004900 0.02604253

Figure

2:

Apartment

Rent

indices (constant$)

II

nil nil

III

nil nil

III

nil nil

nil

III

II

Year

Figure

3: Office

Returns:

updated Kaiser

(1997)study.

0-^CQCM(DQ-^CQC\JCDO^COCNJCDQ^COCnJ<0

0)a)OiO)o>a)CT)CTiOTOioioo)CT>o>0)oio)oo

T--c-r-r-<-T-i-T-^-T-r-T-r-r-T-T--^-,-CMM