A Study of Real Estate Markets in Declining Cities -- Research Institute for Housing America,...

8/8/2019 A Study of Real Estate Markets in Declining Cities -- Research Institute for Housing America, December 2010

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James R. Follain, Ph.D.

research institute for housing americasPecial RePoRtA Study of ReAl eStAteMARketS in declining citieS

0296


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2 A Study of Real Estate Markets in Declining Cities

Rch Ini Hing Amic Dcmb 2010. All igh v.10296

research institute for housing americasPecial RePoRtA Study of ReAl eStAteMARketS in declining citieS

James R. Follain, Ph.D.


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A Study of Real Estate Markets in Declining Cities

Rch Ini Hing Amic Dcmb 2010. All igh v.

Rch Ini Hing AmicBoard of Trustees

Chair

T Byc, Eq.

Radian Group Inc.

Michl W. Yng

Cenlar FSB

Nnc Mll

Wells Fargo

Ew L. Hly

Avanath Capital Partners LLC

Sv Gv

Principal Real Estate Investors

Dn Ycm

IMortgage

Staff

Jy Binkmnn, Ph.D.Senior Vice President, Research and Business Development

Chief Economist

Mortgage Bankers Association

Michl Fnni, Ph.D.

Vice President, Research and Economics

Mortgage Bankers Association


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AcknowledgmentsI m piclly gl Michl Sklz Clll Anlyic (www.clllynlyic.cm )

n Dnni Cpzz (www.n.cm/Dnni_Cpzz.hm), bh whm pvi nil

h pp. I l bni m vl icin wih Gg Gl, wh h j cmpl

nw bk b h xpinc n h clic nin clining ciy Di. Bb

Fllin pvi xclln inc in h li viw n in inl iin. Michl Fnni

w hlpl in vy ph h pjc m i iniil cncpin h inl vin. I wl l

lik hnk G Cc n Cli Jn MBA hi in pcing h inl cmn.


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Executive Summary 7

Section I: Introduction 11

Section II: Types of Declining Cities 13

Tiinl Nin Dclining Ciy 13

Envinmnl Di: Nw Oln 15

Oh Typ Dclining Cii Emging 16

A Lk Sm Pn v h L 40 Y 18

A Cl Lk Svn M A 21

Section III: What Happens to Real Estate Values in Declining Cities? 25

A Lk Simpl H Pic Ml h Svn M A 32

Cnclin Rging Qin #1 33

Section IV: What Is the Range of Experiences for Neighborhoods

within Declining Cities? 35

Ching M Nighbh Dclin 36

2000 Vcncy D 37

USPS D 2008 n 2010 42

Anlyi Sckn n Clvln Hing D 46

Cnclin 54

tAble of contentS


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Section V: Can We Predict with Confidence the Potential

of Substantial Neighborhood Decline within Declining Cities? 55

Anlyi 2000 Vcncy D 59

Anlyi Clvln / Sckn D 60

A Dicin Tw Plici Hlp Hm By n Ln Cpwih h Unciny Sning Nighbh Chic in Dclining Cii 64

Cnclin 68

Section VI: Key Conclusions and Some Implications for Policy 71

End Notes 75

References 79


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Th G Rcin 2007 2009 h kn g ll n hing mk in m cii

n mplin in ll p h cny. Thgh h pc n xn h vll

cnmic cvy h mk i ill m cin, mny plc will likly m gwh

n lly cv wihin h nx c . Thi i lm cinly n b h c ll

mplin . In c, nmb lg mplin iicl (MSA) xpinc

v cin ing h l hl h 20h cny n pi h G Rcin n

nv lly cv k mny y . Evn mng h m wih livly

bigh lng-n ppc gwh, cin bmk wihin hm my min wll blw

cn h pic pk mny y cm.

Wh i clining ciy? Simply p, clining ciy i n in which h ppl hv l, b h

h, pmn biling, ic n n min. A h xm, hink gh wn

m h Ol W, wn h l i n bing. A h cii lg m in h

Uni S ik ipping bck in h ( h wmp) y? Pbbly n, b h

cinly nighbh n bmk wihin m h hv p ipping pin,

n hv lil ppc ning nyhing cl hi pvi pk. Lly, nh yp

clining ciy my l b mging plc h gw bnilly ing h hing bm n

nw xpincing npcn clin in h pic n inc in cl.

Th pimy gl hi pp i inigh n h pnil vlin l

mk in cii h in h mi v n pin cnmic clin. Thgh viw

miv n inicipliny by pi ch, nlyi nw mpiicl vinc n h

xpinc mny lg U.S. m v h p 40 y n c n h xpinc

vn lg m inc 2000, incling ing h G Rcin, I k nw

h qin:

Qin 1: Wh hppn l vl in clining cii, pcilly h h

hv xpinc bnil n pin clin in pplin n mplymn?

executive SuMMARy


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Qin 2: Wh i h ng xpinc nighbh wihin clining cii?

Qin 3: D w knw ngh b bl cninly pic which nighbh

m likly xpinc h v n pin clin?

Th nw h qin b pn viw h li n nw mpiicl nlyi.

Svl in c iliz in highligh m h chllng n ppnii iinl mpiicl wk in hi il inqiy. O picl in h l w

qin i h incinc xm cm h hn h lng-m vibiliy nighbh

bmk wihin clining cii.

My cnclin llw:

Qin #1: N piingly, hm pic clin hply in mk h bnil

n pin c in pplin mplymn. Sch c in pplin n

mplymn igg clin in h mn hing, n bc ppl m mbil

hn h, i k mny y pply n mn bcm blnc gin n

h pic n pi lvl. Thi ining i pp by ng niv n

mpiicl vinc: hm pic clin mch g xn whn pplin ll hn

hm pic inc whn pplin gw. O c, bnil n pin clin in

hing mn cn b ivn by c h hn pplin. I h h c n

pinly in clin, hn h pic clin in h n n b pin ih.

Hnc, h ky ny c h pic in mny mk y pn pn

hing mn in h mk liv h ch pi h G Rcin. F

h mk in which hing mn m likly min blw pvi pk lvl,cvy in hi l mk will b lng in cming. F xmpl, Sckn, Clini,

which i ky c y in hi pp, my bcm nw yp clining ciy bn h

G Rcin n h hing bm n b h 2000.

Qin #2: Th mpiicl vinc n clining cii ngly cnim wily vying

xpinc mng bmk mplin h xpinc pin n ngiv

hck hing mn. In, m h nighbh n bmk xpinc

clin v h hi vibiliy m qinbl , minimm, h h

cvy will b pc. Thi ining i highligh by h xpinc Clvlnn Sckn ing h 2000, bh which incl nighbh h hv xpinc

clin in h pic n wlh mch m v hn h m vg. Oh vinc

bin ing h Uni S Pl Svic Vcncy Svy h pi 2008:Q2 h

2010:Q2 inic nighbh ching ipping pin wih pc vibiliy. Th vcncy

vinc l gg h impnc mving byn h n iniin vcncy

n n ccpying h ni h im n inviw n h incp inmin

b h in h vcncy.


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Qin #3: I pv b vy iicl cninly pic whh mk bmk

i in pmnn mly mpy clin. Thi i piclly blm c

pin clin cl b l-lilling. On h ml vlp k pic c-

cinl viin in h 2000 inil vcncy h 6,500 Cn c wihin h

29 cni c pn in hi y. Th picin bnil, n vg, n

piclly lg h Cn c wih livly high vcncy ; h i, hml livly p jb iming h vcncy h nighbh h

li wih pc vcncy .

Thgh I n hink clin in hing mn will b pin in mny mplin mk, hi

i gh cll n h b pinin will cgniz h impnc pciic lcl mk cniin.

Th nly in hi pp gg h will b n ly hv bn bnil h h

vibiliy cin nighbh. Thi, I hink, i ciicl pin h will likly b wll n by

pnil hm by n ln, wh wn vi plc plg by high cl, vcnci

n iing hing ck minnnc. Th lip i hi picin i hpnil by n ln will v h mk wh inmin b h nighbh

viliy i ily vilbl. Nighbh chic i likly bcm n vn m impn

cmpnn hing ciin in h mk piclly h-hi by h G Rcin.

Pblic plicy my b bl ply n impn l in hlping pnil hmby n ln mk

pn ciin b nighbh chic, b will qi plicymk ckl iicl

ciin b wh g c. Thi i cnin hm in h b n clining cii

in mny p h wl. Gvnmn will l hv hw h-m cc in b

cninc b h pnil lng-m cvy. Pcicl ppl will wn vinc civn plici p in plc. A ch, h vinc m hm p by h

U.S. Dpmn Hing n Ubn Dvlpmn (HUD) i mv in h igh icin.

Tw pciic plicy i ic highligh h chllng. Th i pin n pc

ppil ginc h cll n mn h nighbh in which h ppy i

lc n, in picl, whh h mk i in clin. Impvmn in ch ginc hl

b gh. Th cn i h mbii nighbh bilizin pgm bing pp by

HUD. Thgh ch h plici my bcm hlpl l, miicin my hlp hm mv

w m py cvy.

Mlipl glin my n b -xmin in ligh h chllng: cing ln mk

iky ln in clining mk w cinly n h inn h Cmmniy Rinvmn Ac

(CRA) n h lgilin. Appi n ln wh ci h nighbh i n h vg

bnil clin cn xpc b chllng n i lning gn i h nighbh

cnin bnil pplin miniy hhl. Abn ng n cmplling vinc,

h gn will b cpibl ip impc clim. Ln my hv bnilly ighn


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nwiing n in ll nighbh wihin mplin mk piclly h hi by

mj ngiv cnmic hck nil i bcm cl which nighbh min vibl. Sch

bhvi, hgh jiibl by ln king minin pn ik pil, incling ci,

gly, n liigin ik, my in il hin cvy h m n nighbh

m in jpy. Thi i n in which h gvnmn my b hlp by pviing m ,

gilin c n viniy n. I i likly n qin whh ci pplywill b c in h mk, b by hw mch n by wh pc.

Lly, h nmnl pblm cing y nw b clining cii n hi nighbh

m vy imil pblm in mny p gvnmn king mng cnmic

cvy. Th i, invmn n lning ily hmp by g nciny, which in

il hin h p cvy h nw nml. B n nlyi will hlp vyn

bcm m cnin wh w h. D h h pgm hl b m

m vilbl wi ng iniin cmmi bjciv nly h pgm. Abn

ch inmin, I h cvy will b lng n h nmb il nighbh ghn hy migh hwi b.


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Th G Rcin 2007 2009 h kn mj ll n hing mk in m cii

n lg mplin in ll p h cny. Thgh h pc n xn h vll

cnmic cvy h mk i ill m cin, mny plc will likly m gwh n

lly cv wihin h nx c. Thi i lm cinly n b h c ll m

cii. In c, nmb lg m hv bn in i cin n hinking in iz

mny y pi 2007 n h, n hi pvi iz my b mny y in cming

my n cm ll. Evn mng h m wih livly bigh ppc gwh,

cin gmn wihin hm my min wll blw h pk in hing pic h ly 2000.

Wh i clining ciy? Simply p, clining ciy i n in which h ppl hv l, b h

h, pmn biling, ic n n min. A h xm, hink gh wn

m h Ol W, wn h l i n bing. A h cii lg m in h

Uni S ik ipping bck in h ( h wmp) y? Pbbly n, b h

cinly nighbh n bmk wihin m h hv p ipping pin n

hv lil ppc ning nyhing cl hi pvi pk.

Wh h min c h gn clining cii? A miv n inicipliny by

li, which i viw in Scin II, viy nw hi qin. Th m cmmn

nin clining ciy i m h h xpinc bnil clin in pplin n

mplymn.1 Sm h xpinc clin bc l in hi cmpiv vng

in h pcin m ky mncing pc lik mbil l. Sm cii lik

Clvln n Di, h i hi iinl nin clining ciy in h R Bl gin

h Uni S, b xmpl ch clining hinking cii cn b n in h p

h wl lik Dn, Gmny. Anh yp clining ciy i gn by nl i.

Th c Nw Oln n Hicn Kin i clic b vy cn xmpl. Oh pnil

c clining cii hv l bn . Sm pin h lng-m n migin m

h cl h wm Sn Bl gin h cny. Oh highligh h mgnic pw

Section i:

intRoduction


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A cn icl by Aln Mllch (2010) bgin wih m xmpl, pciic hiicl pi n

cmplling niv xplin h n h xinc h iinl clining ciy. H

ci h cnmic xpnin h Uni S in h cn hl h 19h cny n h i

hl h 20h cny, which h lbl h nin mncing migh, n i bnil

clin in h l p h 20h cny h pimy c h iinl clining ciy.

Cii ch Pibgh, Di, Clvln n Bl n mll cii lik Yngwn, Ohin Schncy, Nw Yk (h iginl hm Gnl Elcic), pim xmpl

clining cii.

A lng-im in Up Nw Yk n n cnmi, I in hi y b h cnqnc

l cmpiv vng mili n cmplling. Hwv, in ppin hi pp, I

cnc viw lng li c i b clining cii. Ew Gl (2007)

n xclln mmy h hiy n ch ciy in n icl nil Cn Bl Ev Cm

Bck?2 A ky p hi xplnin i h lng-im n hiiclly impn linhip bwn

Bl, Nw Yk n h Ei Cnl, which w cmpl in 1825 n llw Bl pp Nw Yk Ciy pp. Th pnc h cnl llw iinl yp mplymn n

inil gin, in iin h npin g m h Miw Nw Yk Ciy. F

xmpl, Bl bcm mj pc l in h ly 20h cny bc i w livly

y bin in m h Lk Michign n n i in l. In iin, i pximiy

Nig Fll llw i b bnil gn lcic pw. Bl w h 13h lg

ciy in Amic h n h G Dpin. I h inc l 55 pcn i pplin.

Gl inii vi xplnin hi clin, b hi mphi i pn h l in i ciicl

cmpiv vng. H xplin h h ky w h l i cmpiv vng in

npin c. Gwh in il n Bl minnc npin hb. Impvmn

in h nmiin lciciy l c i cmpiv vng in hi iny.

Kn Pllg (2007) nh inighl icin in n icl nil Shinking Cii in

h Uni S Amic: Th C, Th Plnning Si.3 Th h cii Pibgh,

Yngwn n Sn J, Clini. H icin Sn J i piclly hlpl bc i

pvi mn y xmpl clining ciy. Th pimy cnmic vn h l pblm

Sn J w h cm b in Silicn Vlly h bginning h millnnim. A l,

h ciy c v l in i high-ch wk c. Pllg ci p h h ciy Sn

J l 50,000 jb l h cm b. Dpi hi jb l, i vll pplin

cnin gw immigin; hwv, Sn J w ill c by h l mny

high pying ch jb.

Th invigin clining n hinking cii i n limi h y h U.S. xpinc.

In, mny cni l wih h i. Wichmnn (2007) Cnvin Sgi n

Unciny c n p-cili hinking cii in Ep. H c gvnmn

mng mgphic chng in Dn, n Gmny, wh h bkwn h -


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ic cnmy c cnmic clin, inil gin n high nmplymn .

Fm 1989 1999, whn h ciy l pplin -migin n cing bih ,

h miniiv ym w ill ic w gwh bjciv. H mk hi pin

vy clly by pning h nliiclly high pplin gwh pjcin iliz by h

miniiv ym ing h ly 1990. Thn, v h l vn y, h ciy xpinc

nxpc gwh. Pc bbnizin hv n in pc bnizin,n y in Dn, hinkg n clin in cl pximiy pping n

wlhy cmmnii. Th gic chllng i l wih hi pchwk, givn h

vlpmn npicbl.

Oh c i mnin in h cin h llw. Th c i n mny h

h viw cmmnic m cmmn hm, ch h l cmpiv vng

c clining cii. Thy l ypiclly incl bnil mphi n pging h

l gvnmn plici. Mny chmpin h nin h m gh chic m b m by

gvnmn hlp viliz clining cii. Th c i n mk nh ciicl pin:ch plc h i wn pcil cicmnc h mk i h gnliz n iicl

vlp p-wn plicy ll clining cii.

Environmental Disasters: New OrleansOn ccin in h Uni S, cii hv xpinc mj clin n nvinmnl

i. Glvn, Tx nm l in h hicn 1900. Th mj n npcn

xmpl in cn mmy in h Uni S i, c, h ciy Nw Oln, which w

pwlly impc by Hicn Kin in 2005. Nm i hv bn cnc h Nw Oln xpinc. Thi bi icin v g i m hi li n

w iic m h m cn 2009 Amicn Hing Svy h cp m

h impc n h hing ck in Nw Oln.

On mj y Nw Oln n by h Bking Ini n nil Nw Oln Fiv

w cnly pblih n i ining vilbl h Bking wb i.4 Rh hn k

mmiz ll hi wk, n h i nlying h nlyi i cib h nin

ilin bn cnmy. A viw h cmic li n c i by Amy Li n Allin

Ply (2010) cib c chciic h cn inc h biliy m bb,minimiz p piivly ngiv hck n b ilin. Thy : i) ng n iv

ginl cnmy; ii) lg h kill n c wk; iii) wlh; iv) ng cil cpil;

n v) cmmniy cmpnc. Th bcm c nin in hi nging nlyi Nw

Oln p-Kin hiy by Li n Ply (2010) n hi Bking cllg.

Oh hink nk hv inigh wll n h Nw Oln xpinc vlp

ln h cii. On xmpl i pnin by Ann C. Kbich (2008) h Ubn


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Ini, which w pn h xpinc wih cvy pin in Nw Oln n wh

h pciv h nin bwn h-m n lng-m gl n ccmplihmn. Th

ln h mphiz i hi: ppl n h-m pg inc hi ih in h

pibiliy lng-m cvy.

Th Nln Rckll Ini Gvnmn pc i igh p incling n

nil Wh in Chg? Wh Shl B? Th Rl h Fl Gvnmn in Mgi:

B n Ln m Hicn Kin, by Rich P. Nhn n Mc Lny.5 Thi y

g h h w w i. Th i w h immi cii c whn h hicn

m lnll. Th cn w h iicly vi lvl gvnmn h in wking gh

pn h cii.

U.S. Dpmn Hing n Ubn Dvlpmn (HUD) Scy Shn Dnvn

imny n h iv-y nnivy Hicn Kin.6 H h ky l hing in

cmpl cvy n h impnc biling h gin hing ck mili wh wn

n. Th cmmniy n pin h l hl i in, b i nw bck v 90 pcn

i p-Kin pplin. On h m impn chllng cnly c i vcn biling

n bligh c h mplin , wh HUD im h 79,000 bligh ni y.

Sn hi imny, HUD l l i 2009 Amicn Hing Svy Nw Oln.

Th ll l vilbl HUD wb i.7 Fllwing w kwy h cmp h

hing iin in 2004 v 2009. Th l hing ck i wn by b 15 pcn i

cn iz 511,600 ni. Rn-ccpi hing h kn h bigg hi; i h clin by

lm 23 pcn i cn iz 142,600 ni. Th nmb hing ni in mll mlimily

c wih iv nin hing ni, which n h hm lw- n m-incm

hhl, h clin by 32 pcn. A l, n pi h l in pplin, h min

n n-ccpi hing ni h inc by 46 pcn i cn min vl $876

n i nw b 12 pcn high hn h min n h ni nin.

Jy Binkmnn, Chi Ecnmi h Mgg Bnk Aciin (MBA) n W D. Rg

(2006) ckl h iicl i ing h c biling h hing ck in 2006 icl,

which i vilbl h MBA wb i.8 Thy highligh w piclly ining pin h

hin cvy. On w h lck l innc by 30,000 m mili h xpinc

l mg. Anh i h ip impc wihin h Nw Oln . A picl c

imbmn ppl wl c $9 billin h ni n $4 billin h ix h

hi zip c.

Other Types of Declining Cities EmergingByn h h cl-c c clining cii, h li n bnizin n cnmic

ggphy incl icin h c h cn l bnil pplin clin h


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iicl v pic. On h inllcl l in hi il i Pl Kgmn, wh

wn h 2008 Nbl Piz in Ecnmic hi vlpmn n chmpining nh pnil

iv bnil ginl cnmic gwh n clin gglmin cnmi. H g

h cii hiv n niy, hwv, i nh ciy cn bin h m b vng, hn

h cn b bnil n ivibl hi m h pvi king ciy h nw n. In h

cnx h Uni S, hi n pin gwh in wn cii liv h lg cii h Nh lik Nw Yk n Bn, h hv bn h king cii v w cni. H

plc hi pibiliy wihin h cnx h nging b wihin iinl hy h k

xplin why vlp cni bn n pciliz in h pcin bl g

n vlping cni l n pciliz in gicll pcin. Kgmn g

h iinl hy n xplin why, in liy, wl i min by ich cni

ing imil g wih ch h. F innc, cny lik Swn xp Vlv n SAAB

c b l imp BMW n Ty c.9 Kgmn nh xplnin gglmin

cnmi, which yp cnmi cl. Thi cp h nin h h ix c

pcin in picl ini n lcin cn b gly c in wih bnil

ch n vlpmn. H bliv ch gglmin cnmi impn in xplining

h bnil i in bnizin hgh m p h wl in h p w cni.

O c, h lip i hi y i h cii lin pn ch cl cnmi cn g

clin i h cii bl chiv bnil cnmi cl by, y, bnil invmn

in ch n vlpmn, nivii n wll-c wk c. M impnly, in my

viw, hi hy ly h mwk n bnil clin wih h ppc ll

cvy nc, xmpl, h mj cii h w in h kin gglmin cnmi

lng njy by h E C king cii.

Gl (1998) ly b cipin h c n bni ciy li n wh h cni b

ciicl ingin bn gwh h bni gglmin cnmi. Whil cknwlging

h h c bn li my inc m pin, h m cnjc b h .

H i piclly pimiic b h livly hmgn n lw niy gglmin h

wn Uni S. Thgh h n mnin Silicn Vlly pciiclly, hi m b n

xmpl wh h h in min. Th cii mny h cnmic vng gglmin

whil l cing h c cngin n cim. Tn y l Gl n Glib (2009)

n xniv li vy n nw mpiicl vinc h gin chmpin h impnl gglmin cnmi ply in h gwh cii. Th lg by vinc pp h

viw h cii cc by ping h n inmin (p. 1023). Thi mpiicl vinc

c pn h impc ch gglmin n h gwh cii in h hn n wn

p h Uni S n h nging hi in pplin w wih high mp

in Jny. Thi y i n pimiic h p, m hgn, l cii h E

n Miw, which will hv m ggl bc hy will b l likly b h mj

c innvin.


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iv-y pplin chng cmp. Th MSA nk in m hi lg iv-y

pplin clin. 134 h MSA xpinc iv-y clin l n pcn inc 1970.

Tbl II-1 cnin h l h wih pplin l 500,000. Twny-w MSA i

hi pn.

Th p h li by lg mn i h Nw Oln MSA, which ving l

ing n Hicn Kin in l Ag 2005. 1,836 ppl w kill n v $80

billin in ppy mg w c. Th pplin in 2005 w 1,312,400; h llwing y

pplin clin 992,000. Thi m h pi m 2001 2006 h n wih h lg

iv-y pcng clin 27.88 pcn. Th cn pplin im 2010 i 17 pcn

high hn h gh in 2006 b i ill b 12 pcn h cvy i pplin

h im h hicn. In c, Nw Oln wl hv ily qlii clining ciy by h

cii in Tbl II-1 wih Kin. Pplin clin by v pcn in h pi 1985

1990 n h n lly cv i 1983 pk pplin 1.33 millin by h im Kin hi

in 2005. In 2010, hi pi ln wl hv plc i mng h p iv w MSA pplinclin inc 1970 mng h wih pplin l 500,000.

Th nx 10 MSA n h li, wih h xcpin Nw Yk, i h cipin h iinl

clining ciy ic bv. Thy incl Bl, Pibgh, Yngwn, Clvln, Di n

h mili xmpl clining cii in h R Bl. Cni Clvln, which xpinc

i w iv-y pplin clin m 1971 1976 4.08 pcn. I cn pplin i

94 pcn i pplin h n h pi (1976) n 90 pcn i pk pplin in

h l 40 y (1971). Th y i imil h h iinl clining cii in hi li. Th

Nw Yk MSA i h lg n hi li by n i n h li bc i pplin clin inh ly n mi-1970. Th l y hi pi clin w 1977. Sinc hn pplin h

gwn xc by hlhy mn i pplin in h mi-1970 n h xpinc piiv

nnl gwh ch y inc h l 1980.

Th bm p hi li incl m m h migh l i h cipin h iinl

clining ciy n m h n. F xmpl, Oklhm Ciy i n h w piclly

h-hi by h il n ving n ln hck h hi h Shw in h ly 1980; hwv, i

pplin y xc i pplin in h l 1980 by v 27 pcn, inicing h ng

cvy h kn plc. Thi c n h n h li highligh impl c b U.S. cii: v pplin c i n icin cniin pin n ivibl clin.

Ovll, hi mpiicl xci cnim li l pc h lbl clining ciy n

h c h pplin gwh hm, vn ing h livly pp y inc 1970, i

mch lw hn in mch h h nin n inicin n hm hi pvi

pk. On h h hn, kp in min h hi li nly cnin ix h ill blw hi

pplin lvl h bginning h pi clin, which i mll cin h 340-pl


20/84



tb ii-1

lrg mrp ar w m sub

Fv-Yr Ppu D 1970

Current Current

Minimum Average Max Population Population to

2009 5 Year 5 Year 5 Year Growth Peak to Population Lowest

5 Year Population Population Population in the Population Population in Year of Population

Estimate Growth Growth Growth Following in Last in Worst Worst in Last 40

Name (Thousands) Rate Rate Rate Min Year 5 Years 40 Years Decline Decline Years

New Orleans-

Metairie-

Kenner, LA 1146 -27.88% -0.49% 7.24% 2006 -16.69% 1,329 992 117% 117%

Bualo-

Niagara Falls, NY 1124 -5.66% -2.42% 1.64% 1980 -5.65% 1,355 1,242 90% 100%

Pittsburgh, PA 2353 -4.99% -2.11% 0.69% 1988 -4.44% 2,761 2,480 95% 100%

Youngstown-

Warren-Boardman,

OH-PA 564 -4.78% -2.22% 2.26% 1987 -4.57% 681 619 91% 100%

Cleveland-Elyria-

Mentor, OH 2088 -4.08% -1.26% 2.16% 1976 -2.83% 2,319 2,220 94% 100%

Detroit-Warren-

Livonia, MI 4430 -3.79% 0.02% 3.44% 1984 -3.35% 4,499 4,183 106% 106%

Scranton-

Wilkes-Barre, PA 549 -3.24% -1.23% 1.91% 2001 -3.04% 608 556 99% 100%

New York-

Northern New Jersey-

Long Island,

NY-NJ-PA 19,098 -3.14% 1.47% 4.51% 1977 -2.95% 19,191 16,601 116% 117%

Dayton, OH 836 -2.95% -0.17% 2.53% 1977 -1.96% 858 825 101% 102%

Syracuse, NY 643 -2.85% 0.11% 3.31% 1998 -2.61% 670 651 99% 102%

Akron, OH 699 -2.61% 0.42% 3.50% 1978 -1.46% 700 659 106% 108%

Tulsa, OK 923 -2.24% 6.05% 15.30% 1989 -1.31% 931 757 123% 164%

Baton Rouge, LA 786 -1.85% 6.57% 13.85% 1990 -0.25% 799 625 128% 173%

Philadelphia-Camden-

Wilmington,

PA-NJ-DE-MD 5865 -1.66% 1.26% 2.99% 1977 -1.31% 5,892 5,266 112% 112%

Milwaukee-

Waukesha-West

Allis, WI 1557 -1.61% 1.32% 4.52% 1977 -1.49% 1,564 1,393 112% 113%

Oklahoma

City, OK 1219 -1.57% 6.46% 14.84% 1990 -0.01% 1,231 973 127% 176%

Springfeld, MA 689 -1.52% 0.83% 4.08% 1980 -0.72% 690 647 107% 108%

St. Louis, MO-IL 2829 -1.36% 1.52% 2.73% 1975 -1.19% 2,842 2,501 114% 114%

Chattanooga,

TN-GA 522 -1.34% 4.15% 7.52% 1986 0.50% 525 425 124% 142%

Toledo, OH 649 -1.32% -0.01% 3.61% 2008 -1.16% 662 649 100% 102%

Providence-New

Bedord-Fall River,

RI-MA 1604 -1.31% 1.73% 4.21% 2008 -0.80% 1,618 1,597 101% 117%

Charleston-

North Charleston-

Summerville, SC 658 -1.13% 8.37% 15.45% 1996 -0.38% 672 518 130% 201%

Note: Five-year growth rates less than or equal to 1 percent and 2009 Population >= 500,000.

Source: Calculations with Woods and Poole Data.


21/84



m m which hi li w cmpil. Th i, h clic xpinc h clining ciy,

ing livly lg m wih pplin l 500,000, i livly in h l

40 y. Rcvi cn n hppn.

A imil nlyi iv-y chng in h nmb hhl w l cnc. Th ling

li incl mny h m m h n b pn pplin pn, hgh w

m Up Nw Yk m nw n h li, Syc n Albny. Nw Oln n Bl

gin p h li ing h cii. Anh ining l h nlyi hhl i hw

w m clly xpinc clin in hi hhl pplin vn whn hi l

pplin clin bnilly. In c, ll b Nw Oln n Yngwn hv pplin

bv hi pvi pk inc 1970 n ll hv hhl pplin bv h nmb h

n h w iv-y pi clin. Hnc, h nmb hhl cv m qickly

n m cmplly hn pplin. Thi likly lc h pibiliy h hhl iz clin

whn h mpl hing vcnci, which pmbly lw hing c h wh

min. I my l lc pin i by mny h, incling Gl; h i, h mpl pply vcn hing in clining cii likly c nw mign wih livly lw incm wh

c h lw c hing.

A Closer Look at Seven Metro AreasSvn m lc ciny in pvi cl lk h vi nin

clining ciy. F ncmp cii n bn h i h nin h iinl

clining ciy m h R Bl: Albny-Schncy-Ty, Nw Yk; Clvln-Elyi-Mn,

Ohi; Di-Wn-Livni, Michign; n Pibgh, Pnnylvni. Th h h mngh -gwing m in h cny v h p 30 y. Tw vy lg: L

Angl-Sn An, Clini n Mimi-F Ll-Mimi Bch, Fli. Th hi i

Sckn, Clini. Th ncmp 11 mll m n 29 cni. Th

hgh h pp highligh inc in hi gwh pn v h p 40 y

n hi xpinc ing h bm n b pi h 2000.

A cmpin h bgin wih lk hi pplin pn inc 1969 ( Fig

II-1). Th p w lin L Angl n Mimi, bh which inc bnilly in iz

in h p 40 y. L Angl inc by 54 pcn, Mimi inc by 148 pcn. Sckni h lin h bm. Thgh livly mll in cmpin h h m in hi gp,

i h gwn by 136 pcn inc 1969. Cn h pn h clining m . Th

Clvln, Di n Pibgh m ll clin in pplin ing hi pi. Clvln

clin by nin pcn, Di by 27 pcn n Pibgh by 15 pcn. Th Albny m

gw by 15 pcn ing hi pi b i incl bc i incl n h b xmpl

iinl clining ciy Schncy, Nw Yk. Th ciy Schncy h pplin

v 90,000 ppl in 1950, 78,000 in 1970 n j v 61,000 y.


22/84



A lk mplymn n mng h vn m highligh n impn pin: pplin

n mplymn cl, b n pcly ( Fig II-2). Whil Di xpinc nly

inicl pcng clin in pplin n mplymn b 27 pcn, h h clining m

xpinc gin in mplymn. Clvln n Pibgh xpinc piiv mplymn

gwh 16 n 26 pcn, pcivly. Emplymn l gw mch m pily hn

Figure II-1

Population Patterns for Seven Metro Areas

Source: Woods and Poole.

20092000199019801970

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000Population, thousands

Los Angeles-Long Beach-Santa Ana, CA

Miami-Fort Lauderdale-Pompano Beach, FLPittsburgh, PA

Detroit-Livonia-Dearborn, MI

Cleveland-Elyria-Mentor, OH

Albany-Schenectady-Troy, NY

Stockton, CA

Figure II-2

Employment Patterns for Seven Large Metro Areas

Source: Woods and Poole.

20092000199019801970

0

3000

6000

9000Population, thousands


Miami-Fort Lauderdale-Pompano Beach, FL

Pittsburgh, PA


Cleveland-Elyria-Mentor, OH

Albany-Schenectady-Troy, NYStockton, CA


23/84



Figure II-3

Inflation-Adjusted Median Sales Prices for Selected Metro Areas (in 2009 dollars)

Source: FHFA.

2009200019901980

0

100,000

200,000

300,000

400,000

500,000

600,000Median Sales Prices, 2009 Dollars


Miami-Fort Lauderdale-Pompano Beach, FL

Pittsburgh, PA


Cleveland-Elyria-Mentor, OHAlbany-Schenectady-Troy, NY

Stockton, CA

pplin in L Angl (91 pcn) n Mimi (223 pcn). Sckn lg pplin gwh

v hi pi w mch lm xcly by i mplymn gwh.

Th l hi iniil cmpin mng h lg m in hi cin c

pn h pic. Th vilbl ply ch h 11 mll m mng h

vn lg bn . Fig II-3 cnin h min l pic m 1980 hgh 2009 j

inlin n in m 2009 ll. 13 Slc vl cnin in Tbl II-2.

Th lvl h pic n hi gwh vi bnilly mng h m in h

l 30 y. Thi i pcilly i nin i c pn h vl h pk h cn

h pic bm, 2006, liv vl in 1980 ( Tbl II-2). F xmpl, l h pic

in L Angl w 285 pcn high in 2006 hn in 1980 n vn cn clin, n

185 pcn hi 1980 vl. In cn, l h pic in Clvln n Di nw

iv pcn lw hn in 1980, hgh hy i bily xc h 1980 vl pi in h

mi-2000. Th c Sckn i piclly iking. Th min h pic in Sckn gw

by 230 pcn m 1980 2006, b i j nin pcn high y hn in 1980 n b ql

h min h pic in Di. Clly, h R Bl cii i n xpinc h gwh h

Sn Bl cii i; b hy l i n xpinc h m g clin.


24/84



tb ii-2

R md hu Pr fr sv lrg mr ar

1980 2006 2009 2006/1980 2009/1980

Albany-Schenectady-Troy, NY $102,271 $187,285 $188,697 183% 185%

Cleveland-Elyria-Mentor, OH $102,245 $107,723 $96,798 105% 95%

Detroit, MI CMSA

Detroit-Livonia-Dearborn, MI (MSAD) $154,328 $200,426 $144,430 130% 94%

Ann Arbor, MI $170,530 $228,164 $183,006 134% 107%

Los Angeles, CA CMSA

Los Angeles-Long Beach-Glendale, CA (MSAD) $209,008 $526,767 $383,913 252% 184%

Santa Ana-Anaheim-Irvine, CA (MSAD) $138,575 $353,238 $246,660 255% 178%

Miami, FL CMSA

Ft. Lauderdale-Pompano Bch.-

Deerfeld Bch., FL (MSAD) $157,204 $335,943 $215,080 214% 137%

Miami-Miami Beach-Kendall, FL (MSAD) $157,887 $326,914 $237,825 207% 151%

West Palm Beach-Boca Raton-

Boynton Beach, FL (MSAD) $169,543 $332,875 $213,190 196% 126%

Stockton, CA $132,139 $303,816 $143,914 230% 109%

Note: Five-year growth rates less than or equal to 1 percent and 2009 Population >= 500,000.

Source: Calculations with FHFA data.


25/84



Thi cin ic n pn vinc b h pn l vl in m

h hv xpinc bnil n pin clin in pplin n mplymn. A pwl

niv n ng mpiicl vinc gg h h pic in ch cii xpinc p clin

ing h pi pplin n mplymn clin; hm, h cvy in h pic i

vy lw n incmpl. Th iniin nlying hi y i livly impl n cnin wih

bic ml pply n mn: ppl mv; h, pmn biling, ic n hpping

mll n. A l, clining cii cn xpc xpinc p clin in h mn

xiing hing, which l mch lw h pic givn h h ck xiing hing i

livly ix. Fll cvy h pic in ch n nvinmn qi ih cin in

h ck hing gnc in h iz h pplin.

Th bi hi picin h bn xmin in lg hicl n mpiicl by li

b h pic liciy h pply hing. Th li ypiclly k xplin h pn

h pic hck mming m n inc in h mn hing, which cn b gn

by inc in h min iv hing mn: pplin, hhl incm n in .

Gnlly, h pic nw hing i livly lic; h i, gwh in mn l bnil

inc in hing pply n livly m inc in h pic. Bil pn

piiv gp bwn h mk pic hing n h c cncin by incing hi

pcin nw hing. A l, bnil n plng bv-nml h pic

gwh inc in mn n h nm lng pc iinl hing i vilbln h gly nvinmn i ppiv iinl hing. I ih h cniin i

n ii, hn xpnin h pply hing i inhibi n bnil n in pic

inc pibl.14

Thi y c n mk in which mn i in pin clin. Wh hppn

h pic in h mk? Gl n Gyk (2005) pn bh cmphniv niv

n cmplling mpiicl vinc h h pic in clining cii cn b xpc clin

Section iii:

WhAt hAppenS

to ReAl eStAte vAlueSin declining citieS?


26/84



hply n cv lwly in hi y bn clin n bn hing. A h c hi

viw i kink pply cv, which i livly l (lic) iin h hing ck n

livly p (inlic) cin in mn. In ch n nvinmn, pin clin in

mn gn bnil clin in pic. Thy w ky pic mpiicl vinc. Th

i xmin pn pplin chng n mn h pplin clin in cii n

b pin l h pplin clin cnibl pi im. Th cnhw h h pic chng i bnilly mng cii in h mi pi bnil

n pin pplin clin v cii h xpinc pplin gwh. Thi mpiicl

vinc invlv h xminin 10-y chng in h pic 321 ciy cl ing Cn

1970, 1980 n 2000. Th c i pn h pn pplin n h pic gwh

ch 10-y pi ncin h gwh in h pplin h cii. Sng mpiicl

vinc n ymmic c pplin gwh pn h pic gwh i pn. In

picl, clin in pplin gwh by n pcn l nly w-pcng pin p

in l h pic v h c. In cn, l h pic gw by nly b 0.23 pcn

p n-pcn gwh in pplin gwh mng gwing cii. Th i, h pic clin v

10-y pi mming m n-pcn clin in pplin i b nin im high hn

h inc in h pic n-pcn inc in pplin h m pi.

Aiinl mpiicl vinc i pn in hi cin h bh pp n xpn pn h

y l by Gl n Gyk. Th i xhibi i impl xminin h clin bwn

gwh in h iz h hing ck n gwh in pplin cni in Nw Yk S

h pi 2000 2008 ( Tbl III-1). Th cni nk by h iz pplin clin

bwn 2000 n 2008. Sixn cni xpinc pplin clin ing hi pi. Amng

hi gp, nly n xpinc clin in h hing ck, n hi w nly .01 pcn

Mngmy Cny, which i livly l cny in h Albny-Schncy MSA. Clly, h

ppl mv b h h ill h.

Th cn pic mpiicl vinc c pciiclly n h linhip bwn l h

pic gwh n chng in pplin gwh. I mi h nlyi Gl n Gyk, b

in n pnilly ich n h pic. Rl h pic gwh i m

ing h Fl Hing Finnc Agncy (FHFA) h pic inx mplin n

pplin m W n Pl.15, 16 Th imin n 132 m wih pplin

in 2008 l 100,000 ppl n wih FHFA pic inx h bgin in 1980 li; h l

nmb bvin i 3,410.17 Th incl y 1980 2007. Th pnn vibl i h

m cn h-y pcng chng in l h pic. Th inpnn vibl incl

h m cn h-y pcng l in pplin gin in pplin. Th m

pplin l i ih ngiv nmb 0; imilly, h m pplin gin i ih

piiv z. Thi llw h ml cp h ymmic impc pplin gin v

l. Th ml l incl ix c bh h m n ch y in


27/84



tb iii-1

cg Ppu v. hug U lrg nw Yrk cu

Ranked Population Decline Percent Change between 2000 and 2008

County Name Population Housing Units

Orleans County, NY 4.6% 0.9%

Erie County, NY 4.3% 1.8%

Genesee County, NY 4.2% 1.4%

Chemung County, NY 3.6% 0.7%

Herkimer County, NY 3.5% 1.7%

Tioga County, NY 3.1% 1.6%

Broome County, NY 2.8% 0.0%

Cayuga County, NY 2.6% 1.1%

Niagara County, NY 2.4% 2.6%

Wayne County, NY 2.3% 3.3%

Montgomery County, NY 2.1% 0.1%

Livingston County, NY 1.8% 4.3%

Oneida County, NY 1.6% 0.7%

Onondaga County, NY 1.2% 3.1%

Oswego County, NY 0.8% 2.0%

Monroe County, NY 0.4% 3.2%

Madison County, NY 0.5% 3.4%

Schoharie County, NY 1.0% 4.0%

Albany County, NY 1.2% 2.7%

Nassau County, NY 1.3% 0.0%

Rensselaer County, NY 1.8% 4.5%

Ulster County, NY 2.2% 5.4%

Queens County, NY 2.9% 2.9%

Washington County, NY 2.9% 6.5%

Westchester County, NY 3.3% 3.5%

Schenectady County, NY 3.3% 2.3%

Putnam County, NY 3.7% 4.2%

Kings County, NY 3.7% 4.0%

Rockland County, NY 4.1% 4.6%

Warren County, NY 4.2% 8.2%

Ontario County, NY 4.2% 7.0%

Bronx County, NY 4.4% 4.4%

Dutchess County, NY 4.5% 6.2%

Tompkins County, NY 4.8% 5.5%

New York County, NY 6.3% 6.6%

Suolk County, NY 6.5% 4.4%

Saratoga County, NY 8.3% 10.3%

Richmond County, NY 9.8% 9.3%

Orange County, NY 11.2% 9.9%

Average 1.4% 3.8%

Source: U.S. Census Bureau, Population Division.


28/84



cnl ll h h vibl h impc h pic. Th l n ky iic

cnin in h i clmn Tbl III-2.

Th l ngly cnim h bic hyphi n h Gl n Gyk l. Th ky cicin

nw 4.32 pplin l n 1.80 pplin gin. Th n-pcn pplin c

i im c h pic by 4.32 pcn v h-y pi wh n-pcn

pplin inc i im inc h pic gwh by nly 1.80 pcn. Thi ymmic

l i cnin wih h bic niv h ppl mv hn l .

Nw nin i n in b l qin: D pi bnil h pic

clin n b llw by nh pi clining h pic? Thi i lm n inv

qin in Fl Dpi Innc Cpin (FDIC) y by Angll n Nmn

(2005) nil U.S. H Pic: D B Alwy Fllw Bm? Thy cncl h b

n ypiclly llw bm ing FHFA h pic inx h pi 1979 2003.

Th m ypicl cm pi bnil h pic gwh i pi livly

l h pic gwh. I imil b in qin: D h pic b lwy

llw h pic b? Th qin i xpl by iming gin qin imil h

n p in Clmn 1 Tbl III-3AB n ing h m xmin hw h-y l

tb iii-2

tg fr ayr Rp f tr-Yr R hu Pr Grw

Ppu G d l

Independen t Variables Dependent Variable: Three-Year Real House Price Change

Last Three Years o Population Change i Negative 4.32

Std Error 0.47

Last Three Years o Population Change i Positive 1.80

Std Error 0.10

Last Three Years o Real House Price Change i Negative 0.72

Std Error 0.02

Last Three Years o Real House Price Change i Positive 0.87

Std Error 0.01

Constant 0.03 0.05

Std Error 0.02 0.01

Observations 3410 3463

R-squared 0.41 0.77

Number o MSAs 132 134

Sample Balanced Balanced

End Year 2007 2007

Fixed Eects or Year Yes Yes

Fixed Eects or MSA Yes Yes

Note: Population o at least 100,000 in 2008 and FHFA House Price Index Data since 1980. New Orleans excluded.

Source: FHFA Price Indexes and Woods and Poole or Population.


29/84



pic chng ncin h m cn n lgg h-y l h pic clin. Th

pciicin incl ix c ch m n ch y in h mpl. Th l

cnin in Clmn 2 Tbl III-3AB.

Th cicin h w lgg vl h pic chng n xhibi h m ymmy

n wih pplin gin n l. Th cicin n h w lgg vl l h

pic chng 0.72, i h lgg pic chng w ngiv n 0.87, i h lgg pic chng

w piiv. In c, hy gg h h impc lgg pic inc i clly lg hn

h lgg pic clin.

H i pnil xplnin h l n why w in n ymmic pn in h c

lgg pplin gwh b n lgg h pic gwh. H pic clin cn b gn

by viy vibl h c hing mn. Th incl inc in in , igh

ci n lw vg hhl incm. In ch c, h pic cn b xpc c,

ll l ql; hwv, clin in h nnpplin c hv n ypiclly bn pin

h pplin c ci wih clining cii wll icl n cmn by

Gl n Gyk n cnim by h vinc in hi pp. I , h pic clin

gn by cin in, y, vg hhl incm inc in in will b l

ymmic inc cin in hhl incm inc in in n ncily

bing b clin in pplin n bil p xc hing ck.

In c, viw h xpinc h m wih h lg clin in h pic pp

hi viw n l highligh h niqn h l w y. Th-y chng in l h

pic w cmp ch h 384 m which FHFA vilbl.18 Th l

pin Tbl III-3AB li h 30 m wih h lg h-y clin in l h

pic ing hgh 2008. All h h clin l 25 pcn v h-y pi.

Th lg clin w 46 pcn Anchg, Alk h pi 1988 1990. N h w

h n h li i h nin iinl clining ciy: Wn, Michign, which i m

wihin h lg Di m n Di il. Th hvily cncn in h

Sn Bl n, in picl, h Shw. Th incl mny h w piclly h-hi by h

il n ving n ln ci h 1980, ch Ain, Oklhm Ciy, Sn Anni n Hn.

In c, h xpinc hi p h cny m h w ginl h pic clin in

h Uni S nil h cn cii n m h bi h h pic cni vl h ci ik h pli Fnni M n Fi Mc. Hwv, h

plc n i h nin h iinl clining ciy n h clin w m likly ivn

by livly mpy clin in hhl incm n iing in ing hi pi,

h hn by pin pplin clin. M impnly, 17 h m h piiv

gwh in h pvi h-y pi n h vg gwh in h pi h y w iv

pcn; hnc, bnil pi h pic clin n pp b ng pic

h pic clin. In iin, mny h plc hv cv h nicly inc h l


30/84



1980 in cn h xpinc iinl clining cii. I viw hi cnin wih h

niv in h pvi pgph: h pic clin n ncily vn lly

l-pping, h hy pn pn wh c hm. I h c i pin clin in

pplin n mplymn h lv bnil xc pply, hn pin clin in h

pic cn b xpc. I, hwv, h c h clin l mpy clin in

h iv hing mn, hn h pic m bl cv wih im in m .

tb iii-3a

mr ar w lrg tr-Yr R hu Pr D

Between 1980 and 2006

Year of Worst Three-Year Real Three-Year Real House Price

Metro Area Three-Year Decline House Price Decline Decline Three Years Earlier

Anchorage, AK 1990 46% 22%

Austin-Round Rock-San Marcos, TX 1990 39% 4%


Oklahoma City, OK 1988 35% 7%

San Antonio-New Braunels, TX 1990 33% 2%


San Antonio-New Braunels, TX 1989 32% 1%

Rockingham County-Straord County, NH 1992 31% 16%

Manchester-Nashua, NH 1993 31% 9%


Los Angeles-Long Beach-Glendale, CA (MSAD) 1995 30% 1%

Peoria, IL 1986 30% 26%



Riverside-San Bernardino-Ontario, CA 1995 29% 6%

Bridgeport-Stamord-Norwalk, CT 1991 29% 44%


Houston-Sugar Land-Baytown, TX 1988 27% 20%

Cleveland-Elyria-Mentor, OH 1982 27% 15%

Detroit-Livonia-Dearborn, MI (MSAD) 1983 27% 15%

Chicago-Joliet-Naperville, IL (MSAD) 1982 27% 17%

Dallas-Plano-Irving, TX (MSAD) 1990 27% 1%

St. Louis, MO-IL 1982 26% 23%

Anchorage, AK 1988 26% 4%

Warren-Troy-Farmington Hills, MI (MSAD) 1983 26% 12%


Houston-Sugar Land-Baytown, TX 1986 26% 1%


Peabody, MA (MSAD) 1991 25% 38%

Corpus Christi, TX 1990 25% 10%


31/84



tb iii-3B

mr ar w lrg tr-Yr R hu Pr D

Since 2006

Year of Worst Three-Year Real Three-Year Real House Price

Metro Area Three-Year Decline House Price Decline Change Three Years Earlier

Stockton, CA 2009 -75% 47%

Modesto, CA 2009 -73% 49%

Vallejo-Fairfeld, CA 2009 -62% 41%

Cape Coral-Fort Myers, FL 2009 -60% 55%

Salinas, CA 2009 -60% 47%

Port St. Lucie, FL 2009 -58% 54%

Las Vegas-Paradise, NV 2009 -54% 52%

Naples-Marco Island, FL 2009 -54% 58%

Riverside-San Bernardino-Ontario, CA 2009 -54% 56%

Bakersfeld-Delano, CA 2009 -47% 61%

Ft. Lauderdale-Pompano Bch.-Deerfeld Bch., FL 2009 -45% 53%

Fresno, CA 2009 -45% 56%

West Palm Beach-Boca Raton-Boynton Beach, FL 2009 -45% 54%

Sacramento-Arden-Arcade-Roseville, CA 2009 -44% 41%

Palm Bay-Melbourne-Titusville, FL 2009 -44% 55%

Oxnard-Thousand Oaks-Ventura, CA 2009 -41% 47%

Reno-Sparks, NV 2009 -41% 50%

Santa Barbara-Santa Maria-Goleta, CA 2009 -40% 41%

Santa Rosa-Petaluma, CA 2009 -40% 34%

Visalia-Porterville, CA 2009 -39% 57%

Oakland-Fremont-Hayward, CA (MSAD) 2009 -38% 35%

San Diego-Carlsbad-San Marcos, CA 2009 -37% 38%

Santa Ana-Anaheim-Irvine, CA (MSAD) 2009 -36% 49%

Detroit-Livonia-Dearborn, MI (MSAD) 2009 -33% -2%

Miami-Miami Beach-Kendall, FL (MSAD) 2009 -32% 51%

Los Angeles-Long Beach-Glendale, CA (MSAD) 2009 -32% 53%

Deltona-Daytona Beach-Ormond Beach, FL 2009 -31% 50%

Phoenix-Mesa-Glendale, AZ 2009 -31% 49%

San Luis Obispo-Paso Robles, CA 2009 -29% 37%

Santa Cruz-Watsonville, CA 2009 -29% 31%

Warren-Troy-Farmington Hills, MI (MSAD) 2009 -28% -1%

Tampa-St. Petersburg-Clearwater, FL 2009 -27% 43%

Note: Population at least 250,000 and Real House Price Decline o at least 15 percent.

Source: FHFA House Price Indexes Adjusted or National CPI

Nw cni nh ln hi mpiicl xci. Whn h li m i cmpil ing

hgh 2009 ( Tbl III-3B), h m wih h w h-y h pic clin

min by h pi 2007 2009. Sckn n Mc, i nby nighb in h n

pin Clini, p h li wih l pic clin nly 75 pcn. M h h n h


32/84



li m in h Sn Bl. Th nly iinl clining cii n h li , gin, Di

n Wn. Hnc, h cn hing cii l cmplly in li wih clin h

p h cmpiin h w h pic clin in h Uni S inc 1980. N, ,

h h pic clin in 2007 2009 ll b h w clining cii w pc by pi

bnil h pic inc in 2005 2007. Th impl vg h pic inc hi

li in h pi 2006 2009 w 45 pcn. Th impl vg h clin h pi 2007 2009 w lm h xc ppi, 44 pcn. Wh w n knw hi im i whh

h pic in h will n v cv. Hiy i ill bing win, hgh m lik

Cli Chn, wh h ml Ecnmy.cm, n pp b pimiic b h pc

cvy mny h .

A Look at a Simple House Price Model for the Seven Metro AreasSm inigh gh by iming impl h pic ml h m ic

li. Th ml xplin h gwh in l h pic ncin lgg hpic gwh, lgg mplymn gwh n ix c ch m n y.

Th 11 m incl in Tbl II-2 h c nin. Th ml i im

ll 11 ing hgh 2007 n hn ply h iinl clining cii n h

Sn Bl cii. Th h pciicin l im ml ing hgh

tb iii-4

sp md f R hu Pr cg fr sv lrg mr ar

Dependent Variable: Annual Percentage Change in Real House Prices (dlog_hp)

Explanatory Variables

Lagged Price Change 0.52 0.54 0.35 0.37 0.48 0.35

Std. Error 0.05 0.08 0.07 0.06 0.10 0.08

Lagged Employment Change 0.63 1.04 0.74 0.92 1.08 0.80

Std. Error 0.18 0.31 0.24 0.18 0.37 0.27

Constant 0.02 0.01 0.02 0.01 0.02 0.01

Std. Error 0.02 0.03 0.02 0.02 0.04 0.02

2005 Year Fixed Eect 0.05 -0.01 0.13

Observations 314 138 176 226 98 128

R-squared 0.68 0.71 0.84 0.61 0.71 0.68

Number o Metro Areas 11 5 6 11 5 6

Fixed Eects or Each Metro Area Yes Yes Yes Yes Yes Yes

Fixed Eects or Year Yes Yes Yes Yes Yes Yes

Ending Year 2007 2007 2007 1999 1999 1999

Traditional DC Both Yes No Both Yes No

Note: FHFA House Price Indexes Adjusted or National CPI.


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1999 cp ny bnil chng in h ml inc 2000. Th imin l

cnin in Tbl III-4.

Fi, I m ck by h imiliy h im cicin n h lgg vl h pic

n mplymn gwh. Th clining m pp b mly m pniv l

c cp by h cicin lgg mplymn gwh (1.04 v 0.74). Th lgg

h pic ppciin i livly m impn in h clining m (0.54 v 0.35),

b h inc n lg n bh bnilly blw niy. Cicin im cl

1.0 (niy) wl b xpc i cn h pic gwh by il w ng pic h

. Eiming h ml ing pi 2000 n h c y h h pic bm

h ly n mi-2000 gn imil im.

Scn, h m mic inc hw p in h ix c ch y . In picl, h

im h ix-y c h Sn Bl m xc h h clining m

in h ly 2000. F xmpl, h ix c h Sn Bl i 12.7 pcn 2005 v 0.7

pcn h clining cii in hi y. Clly, mhing vy in w hppning in h Sn Bl

h w qi h iny n, php, nlikly cp p in h n . My inclinin i

lbl hi ign inl xpcin h iinl xbnc in h Sn Bl in h ly 2000,

hgh h w clly h c, ch h inc vilbiliy mgg ci n bnil

immigin, invlv.19 Ohwi, I w ck by h imiliy h c im h w

gp mingly vy in m wih vly in xpinc ing h p 30 y.

Conclusions Regarding Question #1Th ky pin h nlyi in hi cin llw. Fi, bnil pic clin cn b

xpc cc h MSA lvl in plc h bnil n pin clin in pplin

n mplymn ( Tbl III-2). Scn, h impl c pi bnil h pic clin

i n ncily ignl pic clin n lng n incmpl cvy ( Tbl III-

3). Thi, im livly impl cnmic ml h pic gwh h mll

R Bl n Sn Bl m vl n piclly iking inc miclly

lg im ix c h ly p h 2000, pcilly 2005. Ovll, h l

cnin wih h viw h bnil pic clin cn b ivn by viy c

h c mn, n j pplin mplymn. I h h c n pinly inclin, hn h pic clin in h n n b pin ih. F xmpl, n

m nml h pic xpcin n m nml cc mgg ci liv h

ly 2000 cn b xpc c hing mn n h pic liv h pk y

iinl xbnc. Onc h nw nml h vibl i ch, hing mn my

wll n piiv gwh n h pic will bgin cv. O c, w ill in

h mi h cn mgg cii n m im i n icv wh h nw nml

will b in h piclly h-hi ing hi cii n whn i will cc.


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D m nighbh wihin clining ciy clin m hn h? In picl, m

nighbh xm clin h hn hi lng m vibiliy? Sch iin

mim cll ipping pin, which iin in which picl gnizin

nighbh c cicmnc v i bcm n lng vibl. Wh I hv in min

i mwh lik h ciin bing m wihin mny Chlic ic b which picl

pih cl wn hg pi n ging n iminih cnggin. Thi

picl xmpl i ppn in mny clining cii wihin Nw Yk.

Mch h bn win b vi pn in-mplin clin. Sm h c pn

h clin h cnl ciy lg mplin liv i bbn .20Anh lg

by li xi h c i pciic nighbh clin n h chllng

iniying h ky i vibl nighbh. Thi ppch i pcilly cmmn wihin h

il bn n ginl plnning. Th nlyi in hi cin k pplmn h

impn cnibin in w wy. Fi, h pimy c i pn mll wihin h m

ch Cn c n zip c. Scn, nin i c pn vl pciic mic

l civiy h vilbl lg nmb m n h ppniy

pvi m inigh in h ynmic nlying nighbh chng ing h cn cii

in h mgg n hing mk.

Thgh h cin, mphi i pn h h mic cib n cmn wily

ip in-mplin impc. Th pibiliy wily ip nighbh cm

wihin clining ciy i pcilly impn hhl n bin king mk lng-

m l invmn n piclly impn ln king bil ccl

mgg lning bin. All l ql, ln p n nvinmn wih livly nw

pibl cm bc h lg h ipin pibl cm

picl nighbh, h g h ik ch invmn n lning. Tk xmpl

ln king h mgg ci ik ln in clining ciy. Th ikin i icly

Section iv: WhAt iS the

RAnge of expeRienceS

foR neighboRhoodSWithin declining citieS?


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Cnvninl cmmniy vlpmn n plnning pn hv lk v h pc

pplin lm nivlly, wih lil nin pi hw nighbh phyiclly chng

whn hy l pplin. H gg, n ppch y h phyicl chng ppling

nighbh in nvl wy. Th ppch cni hw pplin clin c in

phyicl impc (m l hing bnnmn, xmpl) c in nighbh.

D pn m il c y Flin, Michign, ill h pplin clin cn bm pinl in m nighbh hn in h, gging h hi icl pp ppch

my b l in implmning m clin. Wh i piclly iking b h ppch i

h nm mn qliiv p kn gn hi gg mic hing ni p

c. Th incl xmining Cn , gn bvin nighbh cniin n

inviw wih lcl icil, in n cmmniy l. Th w lgly cnin

h nighbh wihin Flin. Thgh hi y i vy inighl n cmplling, Hlln

ily mi h i i iicl gnliz m hi picl c y ll nighbh

n ll clining cii, b h ly n civly chmpin h n mbl cii in

vlp n pblic plicy mi.

Th ch in hi pp cni viy nighbh mic n limly l n

: vcncy , h pic, h nmb hing l n h vl h ingl-mily

hing ck. Ech i lc bc hy pn plibl m l civiy h

nighbh lvl n bc vilbl m hm. Th c

h vibl n h icin h mpiicl vinc i gniz n ch c.

2000 Vacancy DataTh Cn-gn vcncy m whh hing ni w ccpi h im h

Cn inviw. I m cp c nin nighbh clin n h xplnin

in h pvi cin b why h pic c in h iinl clining ciy

ppl mv m qickly hn hing ni, pn h gn vcn hing ni. I l

cp h pnil xm cm whn ll m hing i vcn n h nighbh

i n lng cni vibl. Th c inmin hi p h mpiicl nlyi i h

inmin pvi n cmpil h Cn-c lvl. Thi pc incl hn

vibl plc ll v h cny, lng wih mpping cpbilii, which hlpl in

highlighing ip nighbh impc. Th vcncy i cmp h i h l

nmb vcn hing ni in Cn c liv h l nmb hing ni. Th

y y 2000 . Cn-c h vn m ic in pvi cin

h nlyi.21 Th chn in hv mix iinl clining cii

n Sn Bl cii piclly h-hi by h cn hing cii.

Fi, higm h iibin h vcncy h Cn-c lvl gn

h cni: Cyhg, in h Clvln m , Wyn Cny in h Di m , n


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Sn Jqin, in h Sckn m ( Fig IV-1 A, B n C).22 Thy ign cp

h ipin h vcncy in 2000 n highligh pnil inc mng h m

h im. Th ky kwy i h h il h vcncy iibin Cyhg

n Wyn Cni, bh iinl clining cii, mch n il w lg vl

liv h Sn Jqin Cny, which i h nly cny in h Sckn m n i

h pniv Sn Bl ciy in hi gp. N, l, h vcncy in 2000 w l lwn m ighly bnch in Sn Jqin Cny hn in Cyhg n Wyn Cni.

Scn, mp h iibin h vcncy mng ll Cn c wihin Cyhg n

Wyn Cni l pn highligh h bnil ipin vcnci wihin h

(Fig IV-2A n B).23 Th hp cn in h in h pp cn p h mp

Cyhg, which gnlly wihin h ciy Clvln, cp hi ipin nicly. F

h c Wyn Cny, vcncy high in h nhn pin h mp, which

incl mny Cn c wihin h ciy Di. Thgh vcncy ypiclly lw in

h Cn c i h ciy h w, xmpl Cn c wih lw vcncy l ppn wihin h ciy Di.

Mp hlpl wy viw h ipin, b piciv mh bni gly m iicl

m ipin. Th llwing gn wi iicl m h vcncy

iibin cp h xn n chc h ipin vcncy wihin ch cny

Figure IV-1 ABC

Histograms of the Residential Vacancy Rate among Census Tracts for Three Counties

Note: vrate = residential vacancy rate and density in percent.

Source: Stata graphs using Geolytics data.

0

5

10

15

Density

0 .1 .2 .3 .4vrate

A. Cuyahoga County

Density

0

5

10

15

0 .1 .2 .3 .4vrate

B. Wayne County

0

5

10

15

20

25

Density

0 .05 .1 .15 .2vrate

C. San Joaquin County


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Figure IV-2A

Map of 2000 Residential Vacancy Rates by Census Tract, Cuyahoga County

Formula 1

Range From To Count

1 0.00 0.03 72

2 0.03 0.04 72

3 0.04 0.05 73

4 0.05 0.08 72

5 0.08 0.12 736 0.12 0.19 71

7 0.19 2.00 69

Figure IV-2B

Map of 2000 Residential Vacancy Rates by Census Tract, Wayne County

Formula 1

Range From To Count

1 0.00 0.02 89

2 0.02 0.03 90

3 0.03 0.04 88

4 0.04 0.07 90

5 0.07 0.10 89

6 0.10 0.14 89

7 0.14 0.68 86


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tb iV-1

mur f Dpr f 2000 Rd Vy R

cu-tr lv fr 29 cu

Total

MSAs and their Counties Mean Median Std. Dev 95th 99th Skewness Kurtosis Population

Albany-Schenectady-Troy, NY 9.2% 6.1% 8.2% 25.5% 41.1% 2.0 8.4 820,234

Schoharie County 24.8% 15.9% 6.6% 53.1% 53.1% 0.7 1.9 30,546

Rensselaer County 10.3% 7.4% 7.6% 29.5% 32.1% 1.3 4.3 152,349

Saratoga County 9.4% 6.4% 11.3% 26.3% 71.3% 4.0 21.5 204,066

Schenectady County 7.9% 5.0% 6.7% 22.6% 28.2% 1.5 4.3 143,561

Albany County 7.6% 4.8% 17.2% 21.8% 34.2% 1.6 5.7 289,712

Cleveland-Elyria-Mentor, OH 6.7% 4.9% 5.1% 15.6% 25.0% 1.7 7.9 2,106,799

Cuyahoga County 7.7% 5.4% 6.1% 18.3% 29.1% 1.8 7.8 1,349,192

Lorain County 5.2% 4.3% 1.3% 10.9% 26.6% 3.0 16.6 285,644

Lake County 4.0% 3.3% 2.2% 9.2% 10.0% 1.2 3.6 225,403

Medina County 3.7% 3.6% 3.7% 6.4% 6.8% 0.2 2.1 154,795

Geauga County 3.6% 3.7% 1.5% 5.8% 6.0% 0.3 1.9 91,765

Detroit-Warren-Livonia, MI 5.8% 4.5% 4.6% 14.2% 23.1% 1.8 8.4 4,456,076

Wayne County 7.4% 5.4% 2.7% 19.1% 28.7% 1.5 5.7 2,040,413

St. Clair County 7.4% 5.6% 3.9% 14.7% 65.2% 5.4 34.3 166,055

Livingston County 5.7% 4.9% 2.3% 11.4% 19.4% 0.9 4.3 162,974

Lapeer County 5.7% 4.9% 3.1% 11.1% 12.3% 1.1 3.2 89,530

Oakland County 4.3% 3.6% 9.1% 9.9% 15.5% 2.3 11.3 1,199,712

Macomb County 3.3% 2.7% 6.1% 7.5% 10.9% 1.6 7.1 797,392

Los Angeles-Long Beach-Santa Ana, CA 4.0% 3.1% 3.4% 9.9% 16.2% 4.3 39.9 12,518,416

Los Angeles County 4.2% 3.4% 3.4% 10.1% 15.8% 4.4 43.8 9,623,752

Orange County 3.0% 2.1% 3.3% 9.2% 17.5% 4.0 26.4 2,894,664

Miami-Fort Lauderdale-Miami Beach, FL 9.8% 7.3% 8.6% 30.0% 41.6% 2.0 7.8 5,110,265

Palm Beach County 12.9% 10.4% 8.3% 33.6% 46.8% 1.5 5.6 1,154,187

Broward County 9.7% 7.5% 7.9% 29.8% 39.8% 2.0 6.9 1,672,120

Miami-Dade County 7.6% 4.8% 9.6% 27.3% 39.2% 2.5 10.1 2,283,958

Pittsburgh, PA 8.0% 6.0% 5.7% 20.5% 27.8% 1.8 7.1 2,389,583

Armstrong County 10.5% 7.3% 6.8% 38.2% 38.2% 2.5 8.8 72,011

Fayette County 8.8% 7.4% 8.0% 19.2% 29.3% 2.1 8.2 147,277

Allegheny County 8.4% 5.9% 4.0% 22.0% 32.2% 2.0 8.5 1,249,781

Washington County 7.5% 6.5% 2.7% 16.4% 19.5% 1.0 3.7 202,042

Westmoreland County 7.1% 5.9% 5.3% 19.6% 23.8% 1.9 6.5 369,096

Beaver County 6.7% 5.3% 3.9% 16.4% 18.6% 1.4 4.4 175,784

Butler County 5.7% 5.1% 4.5% 11.6% 12.6% 0.9 2.9 173,592

Stockton, CA 4.2% 3.6% 2.7% 9.5% 15.6% 2.4 11.4 593,184

San Joaquin County 4.2% 3.6% 2.7% 9.5% 15.6% 2.4 11.4 593,184

Grand Total 6.0% 4.5% 4.9% 15.7% 24.0% 2.9 21.0 27,994,557

Source: Census and Geolytics.


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USPS Data for 2008 and 2010Th USPS h n in n gmn wih HUD pvi l-im, ib-ich, pcl-

b ym hlp mni n h ciicl i nighbh wih bnil

nmb vcn n bnn ppi. Th p hi gmn i Mplin A

Qly Vcncy Rp. Ech q HUD civ A Mngmn Sym (AMS) ZIP+4

xc inii by USPS ci hving bn vcn n- n ggg

hi h Cn-c lvl l n HUD Oic Plicy Dvlpmn n Rch

wb i. Th USPS Vcncy Svy h bn vilbl v 60,000 Cn c n qly

bi m 2005 hgh 2010:Q2.

Th h piclly civ h liv h Cn-m vcncy

. Fi, hy m h in vcnci. Scn, vcncy inmin i

vilbl bh inil n bin . Thi, hy pvi n xclln ppniy

y h vlving impc ing h cn cii. Ech h i xpli pvi

inigh b h ipin vcncy chng wihin mk xpincing bnil clin

in hi hing mk.

Th USPS vy pvi inmin b h l nmb inil n h nmb

vcn , which llw cmpin Cn-lik m h vcncy , b h

Cn m nly inic whh h ppy w vcn h mpling . Th USPS

m l inmin b h vg nmb y h h ni vcn. Thi

pmi h cmpin m gnl n b m vcnci. Th iniin

in hi cin (VR_y) i llw: VR_y = nmb vcn ni vg nmb

y vcn / (365 nmb ). A ch, VR_y m h cin y h h

inil hing ck w vcn ing y liv 100 pcn ccpncy.25 Thi m

gnlly pvi mch high im vcnci hn h Cn m. F xmpl, h

vg Cn vcncy mng h 64,891 Cn c in h 2010:Q2 USPS w 4.2 pcn

wh h in-wigh vcncy w 7.0 pcn, which i m hn 68 pcn high.

W iliz hi nhnc m vcnci highligh inc in h vi iicl

m ipin n bv. On cmpin c pn h ipin VR_y mng

233 cni wih pplin l 250,000 ppl in 2000. Th iincin i m bwn

h h gw in pplin bwn 2000 n 2008 (189 cni) n h h clin in

pplin ( Tbl IV-2).

Th l mn mch m cnin n xpc pn hn bv wih

h Cn vcncy m. Vcncy mch high mng h cni h hv

xpinc pplin clin hn mng h h l pplin. In picl, bh h

mn n h min h in-wigh vcncy mng Cn c cni

h xpinc pplin clin lm bl h mng cni h xpinc


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pplin gwh bwn 2000 n 2008 (10.1 v 5.2 pcn n 5.6 v 3.1 pcn). In

iin, h iibin vcncy wihin h cni wih pplin clin i mch

m ip, incl mch lg xm vl n i l hn h iibin wihin

gwing cni. Thi pin i cp by inc in h n viin (12.4 v 6.7

pcn), inc in h 95h n 99h pcnil vl (33.5 v 16.8 n 53.4 v 30.5

pcn) n inc in ki pkn (23.5 v 87.2). Th nly iicl m

wih hi pn i kwn, which m h g which h iibin i il

h igh h vg vl w lg vl. I xpc l kwn h gwing

cni, b j h ppi w n.

Th m nlyi i cnc h Cn-c lvl ch vn m chn pcil

nin in hi y. Slc iicl m pn ch h 29 cni wihin

h m ( Tbl IV-3). Th cni nk by h mn vg in-wigh

vcncy ing 2010:Q2 . Th pn in h l iking n m ng

cnimin h cnl hyphi hi cin clining cii hv high vg vcncy

n mny m xm vl iniciv pnil ipping pin. Thi i m vin mng

h cni h ncmp h cnl cii h clining m. F xmpl, Wyn Cny,

which incl h ciy Di, h n vg in-wigh vcncy 27.6 pcn

n iv pcn i Cn c hv in xc 87.4 pcn. Th 99h pcnil vl

i n mzing 111.2 pcn, Cn c wih impl vcncy 40 pcn, b n vg

in inil vcncy h xc 1000 y, lm h y. Th nx h l

i h nin clining ciy cny Cyhg, Allghny n Schncy. Th in-

wigh vcncy xc 12 pcn n hv n viin h w h im

tb iV-2

mur f Dpr f UsPs Dur

Weighted Residential Vacancy Rates in 2010:Q2 among Large Counties in the US

Population Gain Population Loss

between 2001 and 2008 between 2001 and 2008 All Large Counties

Mean 5.2% 10.1% 6.2%

Median 3.1% 5.6% 3.7%

Standard Deviation 6.7% 12.4% 7.9%

95th Percentile Value 16.8% 33.5% 20.3%

99th Percentile Value 30.5% 53.4% 35.3%

Skewness 5.1 3.2 4.7

Kurtosis 87.2 23.5 73.9

2001 Population 1,736,014 1,629,390 1,713,750

N 189 44 233

Note: County Population greater than 250,000 in 2001.

Source: USPS Vacancy Survey.


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inc vy cny xcp S. Cli. Sill, h Sn Bl h y xpinc nyhing lik h

vcncy in h clining cii.

Thi pp, lik m in h li, c pn inil l wing vilbiliy.

Th USPS n nl ppniy cc n vl inmin b nninil

ppy. I ck h nmb bin , h nmb vcn bin n h

in h vcnci. Th m in-wigh m bin vcnci i cmp

ing hi inmin n pvi in Tbl IV-5. Th inmin incl 2010:Q2 wll

chng inc 2008:Q2 n nk by h 2010:Q2 bin vcncy .

A iking h i h mch high bin vcncy hn inil vcncy

. In c, impl invigin h clin bwn inil n bin vcnci

h 6,518 Cn c mng h cni vl ng cnncin. A impl gin

wih cnn m hw h h bin vcncy i b 50 pcn high hn h

inil vcncy n h n R2 50 pcn, which gg ng linhip. Thgh

hi linhip wn m y, n likly inl hi cl linhip i h mny

h bin h mll bin h v h lcl pplin, .g. n,

lni, g in, c. Whn h mn hing in h clin, h mll bin

likly b h-hi wll. Th cnin wih hi xplnin.

Lik inil vcnci, bin vcnci hv in bnilly ing h G Rcin.

A impl gin gin cnim bh h bnil i n ng clin wih vcncy

in 2008:Q2. Th in-wigh bin vcncy in 2010:Q2 i 64 pcn high in

2010:Q2 hn in 2008:Q2 mng h 6,533 Cn c in h gin. R2 h gin i 88

pcn. Thi cgy nninil l h bn hi h n pp b clly l

pn in h inil c.

Analysis of Stockton and Cleveland Housing DataM ic m h viliy nighbh wihin hing mk incl m

h pic, h vlm l n h vl h hing ck. Illy, ch inmin wl b

vilbl viy nighbh n qn im pi, which pmi inigh b hw

h ynmic h nighbh chng v im. Unnly, ch inmin h hiiclly

bn n n nnxin. Dcnnil inmin h yp by Gl n Gyk w

n h b vilbl y ny yp im pn hing h nighbh lvl.

W in h mi vlin in which ch bcming incingly vilbl. Th

pimy c ch inmin cni pblic c n h l n ppy x

c, which hv bcm incingly vilbl vi vi ggg wh n h

gn m vlin ml (AVM) h vl. Th piv vn n cmpni

wh pc n vlp AVM gn incm by lling h n h AVM pc. 27 On


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h ling im in hi n Clll Anlyic i h by m cllg wh h

kinly pvi vlbl hi y.28

Th yp inmin hv bn pvi ch zip c in w h m wihin h

c gp: Clvln n Sckn. D h w q highligh cn pn

in w plc wih vy in hii. Clvln ll in h cgy iinl clining

tb iV-5

mur f cg Drbu f UsPs Dur-Wgd Bu

Vy R fr 2010:Q2, 2008:Q2 fr 29 cu

2010:Q2 Values Change from 2008:Q2

Total Business

Metro Area County Name Mean Median Std Dev 95th 99th Addresses Mean Std. Dev 99th

Detroit Wayne County 62.2% 51.7% 46.0% 154.9% 211.6% 61,223 27.7% 20.6% 94.2%

Cleveland Lorain County 35.1% 31.0% 25.5% 83.9% 108.7% 8,494 13.2% 6.4% 2.2%

Albany Schenectady County 34.3% 25.4% 29.0% 97.2% 107.5% 3,895 12.2% 9.3% 20.2%

Cleveland Lake County 33.2% 32.1% 22.8% 68.8% 118.4% 9,360 14.7% 9.3% 55.6%

Pittsburgh Butler County 32.2% 25.5% 25.4% 99.1% 100.6% 5,321 14.0% 11.0% 41.6%

Cleveland Cuyahoga County 31.0% 26.3% 26.9% 82.6% 128.8% 52,246 11.9% 10.7% 44.2%

Pittsburgh Allegheny County 27.9% 24.2% 23.3% 72.9% 98.2% 50,023 12.2% 8.6% 34.1%

Cleveland Geauga County 27.7% 24.8% 12.8% 45.7% 51.8% 2,723 15.2% 3.7% 18.8%

Detroit St. Clair County 27.6% 26.3% 27.3% 72.2% 121.0% 4,645 6.6% 9.0% 42.1%

Pittsburgh Beaver County 27.3% 19.0% 28.5% 82.8% 138.9% 5,308 12.7% 12.3% 61.7%

Detroit Macomb County 27.1% 25.7% 19.7% 57.1% 84.4% 26,286 10.3% 7.6% 25.1%

Albany Albany County 26.1% 21.0% 24.1% 73.4% 106.6% 11,812 10.4% 7.7% 26.0%

Miami Broward County 26.1% 22.4% 20.3% 59.6% 89.2% 87,680 13.5% 9.1% 33.9%

Detroit Oakland County 25.5% 23.1% 21.7% 68.0% 90.1% 51,233 10.1% 9.4% 34.8%

Albany Rensselaer County 22.3% 18.0% 19.8% 57.1% 73.7% 3,622 11.1% 5.5% 20.1%

Pittsburgh Armstrong County 21.2% 19.3% 16.4% 60.5% 60.5% 2,074 9.7% 6.0% 25.3%

Miami Palm Beach County 21.0% 16.1% 20.5% 60.5% 101.5% 71,266 10.5% 8.4% 41.9%

Pittsburgh Westmoreland County 20.5% 14.2% 22.8% 65.1% 113.1% 12,541 8.0% 9.1% 45.0%

Pittsburgh Washington County 20.2% 15.1% 24.4% 71.1% 101.5% 6,945 8.5% 10.6% 38.9%

Cleveland Medina County 19.1% 9.8% 21.6% 74.3% 83.3% 4,570 10.3% 8.7% 42.3%

Detroit Lapeer County 19.1% 22.0% 14.8% 37.3% 40.8% 2,045 9.5% 7.2% 15.6%

Miami Miami-Dade County 17.0% 13.9% 14.8% 43.7% 72.1% 129,232 8.3% 6.6% 32.5%

Stockton San Joaquin County 16.7% 13.3% 16.3% 47.3% 71.1% 17,918 9.9% 7.7% 35.1%

Albany Saratoga County 12.9% 9.2% 13.4% 38.8% 52.6% 5,574 8.0% 4.3% 15.0%

Albany Schoharie County 12.5% 11.8% 11.6% 36.1% 36.1% 526 9.5% 4.5% 17.0%

Detroit Livingston County 11.6% 7.1% 15.7% 49.9% 71.2% 4,745 8.4% 7.7% 23.5%

Los Angeles Orange County 10.1% 7.6% 11.1% 30.4% 55.8% 125,213 4.8% 4.3% 23.6%

Los Angeles Los Angeles County 9.0% 6.4% 11.3% 25.8% 51.8% 389,970 4.2% 4.7% 22.0%

Pittsburgh Fayette County 8.6% 2.0% 14.0% 46.1% 50.2% 3,811 3.6% 6.7% 19.9%

Note: Ranked by the average duration weighted vacancy rate.

Source: USPS Vacancy Survey.


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ciy. Sckn, n h h hn, h bn mng h gwing in h Uni S nil

h G Rcin n nw i i mng h piclly h-hi by h cii. Hnc, h

w pvi n xclln ppniy cmp hw w in yp plc pm ing

pi clin. Th pvi qly inmin inc 2000 n hgh 2010:Q2

h zip c lvl.

Inmin i pvi b hing l, which cni wh Clll Anlyic in

gl (nn-i m lngh) l wll Rl E Own (REO) n cl

l n l pic, which l vilbl by h h cgi bh m . Sp

inmin i vilbl ingl-mily ncin (n ni) n mlimily ncin, which

ypiclly w- -ni ppi. In iin, pcil n niq clclin

pvi h Sckn m ing Clll Anlyic pciv AVM, which pvi

m h vl ingl-mily hing ck h zip c lvl m 2000:Q1 hgh

2010:Q2. A vl ch ppy wihin ch zip c i pvi by hi AVM ch q. D

vilbl hi y m 2000 hgh 2010:Q2. Thi yp inmin i livly nwn impn vlpmn. Th im cmp ing h m ml n clclin

cnc cn AVM im. Th incl cmpin cmpbl ppi,

impl hnic ml n pplicin wigh p l mh. Th ciicl p i

hiicl im h vl h hing ck n qly bi ing nim vlin

mh. Th im l k in ccn h chnging nmb hing ni in h ck

ch q. Th mpiicl nlyi in hi cin iliz hi inmin inigh b h

wily vying in-mplin pn h pic n l ing pi bh bnil

gwh n clin h w m . On i h clic clining ciy Clvln n h

h, Sckn, h bn hi h ny m by h cn cii n my pn nw

yp clining ciy c by h G Rcin.

Tbl IV-6 incl h vi l ig bh plc ing h c. Clvln l l

gnlly wic h nmb Sckn hgh m h c, y i pplin w h

im lg hn Sckn. Bh inc by b 50 pcn ing h i iv y

h c. Th nmb gl l in Clvln pk in 2005 whil Sckn pk w

ch y li. Th nmb 2010 pn h hgh 2010:Q2.

Th m mic pc h l nmb i h chng in h cmpiin l in h w. Fig IV-3 highligh h chng by pling h h ll ingl-mily l h w

gl nni bh m n cmpi h w. F Clvln, h c

bgin wih gl l h minn pin ll l (b 94 pcn), b gl l

iminih liv REO n cl l v h nx vl y. Th lw pin cc

b 60 pcn in 2008, b h inc inc hn i m. Th pn in Sckn i mch

m mic. Rgl l min hgh 2006 b clin by hl in 2007 n cmpi nly


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11 pcn ll l in 2008. Rcvy inc hn h bn m n gl ingl-mily l

min l hn n-hi ll l.

Fig IV-4 k cp h xn viin in h pic chng mng ll h zip

c in h w m . Th mic i h pcn chng in n vg l pic incliv

Figure IV-3

Regular Share of Total Single-Family Sales for Cleveland and Stockton

from 2000 thru 2010:Q2

Source:Collateral Analytics.

201020082006200420022000

0

20

40

60

80

100Percent

Regular Single-Family Share for Stockton

Regular Single-Family Share for BothRegular Single-Family Share for Cleveland

Figure IV-4Distribution of Zip Code Based Changes in Current Average Single-Family House Values

for all Sales from Peak for Cleveland and Stockton

Source: Collateral Analytics.

0.010.050.100.250.500.750.900.950.99

-100

-80

-60

-40

-20

0

Change in House Values, Percent

Current to Peak: Cleveland

Current to Peak: Stockton


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ll h l yp m h pk y 2006 hgh h 2010:Q2. 29 Th iibin h

chng pl ky pcnil h iibin. Th min clin in Sckn h bn

56 pcn, which i bl h clin in Clvln zip c. Th inqil ng (75h l

25h pcnil) 16 (-25 41) pcn clin Clvln n nin pcn Sckn

(53 62). Fiv pcn h zip c in Sckn xpinc clin in xc 75 pcn.

Clly, h clin bh bnil, n vg, b l wily vying mng bh .

Th vinc m ng pp h bic i i in hi cin h pp, h

wi viin in h cm nighbh wihin clining ciy cn b xpc.

A pvi y nil ZIPpiy D D! by Fllin n Fllin (2007) imil y

ppy pic ppciin in L Angl Cny ing h i ix y h 2000. W gh

whh zip c pning livly lw- n m-incm hhl, m by

Cn inmin in 2000, xpinc high h pic ppciin ing h bm

y h ly 2000 hn in wih livly high incm. Th nlyi n h hy i.

I l vl n ining ci highligh hi ining h zip c wih h lw

ppciin m 2000 hgh 2006 in L Angl Cny w h m 90210 zip c h

incl Bvly Hill n i h nm ppl lviin hw m w y bck.

A impl nlyi i cnc in hi pp Clvln n Sckn. Th highligh pn

in Fig IV-5, which cnin h vg ingl-mily h pic gn by ll h yp

l vl zip c wih livly lg nmb l. On lin pl h vl h

zip c (95376) in Sckn wih h high vg vl hgh m h pi. Avg l

tb iV-6

nubr f sg-Fy s f a typ (Rgur, Reo, d Frur)

fr cvd d sk: 20002010:Q2

Cleveland and Stockton Cleveland Stockton

Total Total Total

Year Regular REO Foreclosures SF Sales Regular REO Foreclosures SF Sales Regular REO Foreclosures SF Sales

2000 32,923 899 1,393 35,215 22,440 560 1,013 24,013 10,483 339 380 11,202

2001 33,605 759 1,329 35,693 23,284 626 1,163 25,073 1,0321 133 166 10,620

2002 36,603 965 1,663 39,231 25,833 860 1,527 28,220 10,770 105 136 11,011

2003 41,611 1,304 2,163 45,078 28,875 1,200 2,039 32,114 12,736 104 124 12,964

2004 47,324 1,558 2,721 51,603 30,707 1,479 2,629 34,815 16,617 79 92 16,788

2005 46,939 2,139 4,043 53,121 31,085 2,086 3,997 37,168 15,854 53 46 15,953

2006 36,997 3,244 5,385 45,626 26,804 3,165 4,999 34,968 10,193 79 386 10,658

2007 25,223 4,640 12,328 42,191 21,320 3,868 8,630 33,818 3,903 772 3,698 8,373

2008 18,298 13,731 18,195 50,224 15,887 5,238 7,383 28,508 2,411 8,493 10,812 21,716

2009 18,514 12,178 11,911 42,603 14,728 4,119 5,184 24,031 3,786 8,059 6,727 18,572

2010 9,408 3,913 5,407 18,728 6,969 1,418 2,602 10,989 2,439 2,495 2,805 7,739

Source: Collateral Analytics.


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pic in hi zip c m hn bl m 2000 hgh 2006 b $480,000 p ncin.

Sinc hn l pic in 95376 hv clin b $200,000 n blw vl h h

c. 95206 i zip c wih mch lw vg vl. Pic m hn ipl bwn 2000

n 2006 m $116,000 $362,000 in hi zip c b bginning hi plng vl nly

lighly bv h h bginning h c. Th, h Sckn l i imil wh w

n L Angl Cny h g ppciin w njy mng h livly mly

pic .

Th pn h pic chng wihin Clvln m qi in b pn viw

h zip c pic . Ppy vl w gnlly lw in Clvln hn in Sckn n nn

h lc zip c hw nyhing lik h ppciin xpinc in Sckn. Zip c

44256 b 25 pcn by 2006 b i nw j bv h vg vl in 2003. Zip c 44102,

which i h lw pic zip c in hi gp hw livly lil mvmn hgh h

c. Fi ll, pic ppciin w mch m m in Clvln. Scn, ppy vl

mng h lw-pic i in Clvln i n i in liv m hy i Sckn.

A cmpl xplnin hi pn i ll implicin i n wih h vilbl.

Hwv, llw m pcl. I cl b h inliny xpcin w piclly xgg

mng lw n m incm in h Sn Bl, n mhing lik ginl cngin c

w pn n l xpcin b h gwh hing mn n h pic.

O c, cc ci ply l in ling mn inc bpim pc w piclly

ppl in Clini. Th m inliny v n ppi bpim mgg pc

Figure IV-5

Average Single-Family House Sale Prices for all Sales Types for Several Large Zip Codes

in Cleveland and Stockton: 2000 thru 2010:Q2

Note: Stockton Zip Codes begin with 95 and Cleveland with 44.

Source:Collateral Analytics.

2010200820062004200220000

100

200

300

400

500Average Single-Family Price, thousands $

95376

95209

95336

95206

4425644107

44102


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my n hv bn pn in Clvln. Rging h implicin h in pn, n

pibiliy pin h impc h pic clin n hhl wlh. Th pn gg

h lw-n m-incm hmby in Sckn my b piclly h-hi, wh h

pin my b m vnly iib in Clvln.

Dcmning h chng in h nmb l n l pic b pn ncin cp

j p h impc h hing cii n h lcl hing mk. Php m nmnl

m i h impc n h hing wlh in in h wh my nv hv ngg

in ncin. Clll Anlyic civ AVM pvi niq n vy bing m

h vl h ingl-mily ck hing Sckn ( Fig IV-6). Th i inic

h vlin ingl-mily hing ck m 2000 hgh 2010:Q2 i ivn by w mj

c: chng in h nmb ingl-mily ppi n chng in h vlin h

ppi. Th nmb ingl-mily ppi h bginning h c w 118,000 n

inc b 152,000 in 2010. Th vl hi ck hing bgn h c wih vl

b $17 billin n h min vl h ni w b $156,000. Th vl hi ckinc b $65 billin in 2006 bc inc in bh h nmb ni 148,000

n hi min vl $415,000. Sinc hn h vl h ck h plmm in vl $23

billin. Thi i b $6 billin 35 pcn bv i 2000 vl. Ab iv pcn h chng

i n inc in h nmb ni inc 2000. Th i b 30 pcn inc

in min vl, which w j b h m inc in h vll cnm pic inx h

Uni S inc 2000.

A hm hi cin i h cing lly n n ggg h mplin ll j

p h y. Th in-mplin viin ll nh. Eigh pniv zip c

Figure IV-6

Total Value of Single-Family Housing for Stockton: 2000 to 2010:Q2

100

125

150

175

200

2010200820062004200220000

20

40

60

80Value of Existing Housing Stock, Billions $ Number of Housing Units, Thousands

Housing Units

Value of Housing Stock


53/84



lc hlp highligh hi hm. Tw h n in Sckn wih h m vlbl hing

ck 2010 95376 n 95377; h vl h hing ck in h w zip c pn

b 20 pcn h l vl Sckn m hing ck in 2010. Th cl

h vg h n cllcivly pn b 20 pcn h vl h l ck in 2010:

94207, 95209 n 95219. On, 95205, h h lg pcng clin in vl inc i pk ing

h 2000 (79 pcn). On, 95230, h h lw pcng clin in vl inc i pk ingh 2000 (10 pcn). An n, 95236, w lc bc i h n h lw h h

ck (0.57 pcn) n xpinc clin 35 pcn inc i pk. Th vl h ck in

ch h zip c i pl in Fig IV-7 2000 hgh 2010:Q2.

Fllwing m h l h cgh my nin n h highligh h wi viin mng

zip c xpinc. Th vl h hing ck in zip c 95376 bl in vl bwn 2000

n 2005, b h vl i ck hing h inc clin lvl blw i vl in 2000. In

c, h h zip c nw hv lw vl hn in 2000: 95376, 95207 n 95231. Th vl

h ck in 95230 h inc by v 115 pcn inc 2000 n h nly givn bck 10 pcninc i pk in 2005. Vl in 95320 l p by v 100 pcn inc 2000, b xpinc

53 pcn clin inc hi pk.

Sch miv ll c i in hing wlh mng Sckn hmwn likly h n

impc n hi pning n h lcl cnmy. In, mch h bn win b h mginl

ppniy cnm hing wlh.30 Th m m n hi i i Aln

Gnpn, m chimn h Fl Rv B. H n cllg h F, Sni

Ecnmi Jm Knny, i n xhiv y h Sc n U Eqiy Excin

Figure IV-7

Changes in the Value of Single-Family Housing Stock

for Several Representative Zips in Stockton

20102009200820072006200520042003200220010

1

2

3

4

5

6

7Value of Single-Family Housing Stock, Billions $

95376

95377

95209

95219

95207

95205

95236

95230


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h U.S. cnmy whl. Amng hi ining i h qiy xc m hm w

innc b n pcn pnl cnmpin xpni m 1991 hgh 2005. I h

bn piclly impn inc 2000. Eqiy xcin innc b 0.6 pcn pnl

cnmpin xpni in h 1990 n v 1.75 pcn bwn 2000 n 2005.31 I h

nmb pply cnm in h Sckn n pply ing pi p cin

in hing wlh, hn h impc h nly $40 billin clin in h vl wl p i n in hi pning. Al, mny h wh xc hi qiy h pk h

bm likly ply nw in m hi hm qiy.

ConclusionsThi cin vl m n c mic nighbh chng ing

h bm n b pi h p 10 y. A high lvl, h l ngly cnim h

nighbh wihin clining m cn xpinc wily vying xpinc. Th xn

h clin i n h qlly ppinly. Th b xmpl in hi cin myb h xpinc h zip c lvl Clvln n Sckn, which highligh wi inc

in h pic chng n hing wlh. Th nlyi vi vcncy- m i nh

hlpl xmpl bc i gg h impnc mving byn h n iniin

vcncy n n ccpying h ni h im n inviw n h incp h

in ni vcncy. Th in-wigh m vlp wih h USPS Vcncy Svy

m pcilly hlpl in h iniicin nighbh wihin clining cii n

hi ipping pin.

Th nlyi l highligh h inhn cmplxiy ming n mniing nighbh pn.Lking w, nlyi l mk gh b bl bcm m gnl

hn w pibl j 10 y g bc g impvmn in h qliy n vilbiliy

pblic c n h inmin in h USPS Vcncy Svy n h c. Ggphic

Inmin Sym (GIS) mpping chniq my b hlp wll. On h h hn, h

i ill n mj limiin in h vilbl limi im-i high-qncy inmin

b h i hhl in h mk. On hi i h i l m m pimim inc

h l h 20052009 Amicn Cmmniy Svy, which b l in Dcmb

2010, will pvi inmin b hhl h cn b mg h Cn-c lvl wih

h h c n php, vn m qn, gnl n cmpl pic

nighbh vlin hn v b.


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Thi cin c pn l b m iicl qin hn h pvi cin; n h i

ciicl h vlpmn ccl hhl nighbh chic, pn ik mngmn

by ln n gvnmn ni chg wih cmbing bn clin. Th qin i whh

hhl, ln n gvnmn hv cn vlp cibl ml cpbl picing which

pciic nighbh likly xpinc h m xm cm, n which incl

h pibiliy bing bnn? Th qin i impn ch h gp viy

n n h impn plicy implicin. Hhl mk hing ciin imln

wih h chic picl nighbh in which liv n i mili. Unciny cn

l cly mik n pic p in nighbh wh m ncin. Ln

king pic mgg ci ik pnly bni m b ml nighbh pic

ipin. Abn ch inmin, ln will b incn vi h nighbh wihpiclly ncin , vn i glin phibi ch cin. Gvnmn icil c

imil chllng in ciing hw llc cc c pm nighbh inbiliy.

In, n h hm in HUD cn nighbh bilizin pgm i vinc m.32

Mlipl glin my n b -xmin in ligh h : cing ln mk iky

ln in clining mk w cinly n h inn h Cmmniy Rinvmn Ac (CRA)

n h lgilin.

H i n xmpl h my hlp xplin wh i k. Ci c n highly cl

wih cil n hnic chciic. Ci c l n b n impn iv mggl. Ln hv bn bl incp ci c in h picing mgg ci ik bc

bnil n cibl mpiicl vinc h bn ccml mn h lgiim

bin miv xi chg high mgg h wih lw c vn hgh hi

mn h, n vg, cin cil n hnic gp will py high hn whi nnHipnic

bw, ll l ql. Abn ch vinc, hy bjc clim h ch plicy h

ip impc n miniy bw.

Section v: cAn We pRedict

With confidence the

potentiAl of SubStAntiAl

neighboRhood decline

Within declining citieS?


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Th m kin i ppli n nlyi pnil nighbh clin i ch clin n

b highly cl wih h cil cmpiin nighbh. I ch clin xi,

gl will qi ln pvi ng vinc jiy inil picing mng

nighbh. Abn ng vinc iingih bwn h c cnmic c clin

wihin nighbh n hi cil cmpiin, ln my ch bnilly inc

nwiing n in

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