反向巢型多項式Logit 模型下的住屋需求與租買選擇

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(2) ~. - 138 . * (:tm1t~J£* ) ;ffi~~ , INffijAfFl:tE~~Jtf±~Zm.*/N~ , jM1itt!2it - fjj:~ :@Jt~. ~~, BPflUl{~~. ( tenure choice ). a. m:tm~:tEr~,:c,,*@IB t:p ~mtJj. IY-J ,j1}'~Wf~~~¥~~~ilj fJrili~~OJti~Jtt£~mJ1fj~~ ftStrW:aJjH~Wm~m-~Zra'ft~t!ft-J* 'J\ J'l~ft-Jm:tEPJ~. a. ,m::r;tt~ AJ[~. ~~J[tl J:Aij@~IT.tlL~rrfHH~Wt.. , ~1!I!1ipHlrP~zra' ft-J~ftM:~ , ~[]1t*1f)(lt* t:p f5~t&t1~*&,?t,~. .JJt~fimIl(f{]~&. 0. ~7~iRllt - rlmm. '. *)(~±~ § ft9Hfl:tEm-.:R~!H.Il1im ~Jij. EI fi mJiitz it~tE tn:J J2.{ 13 IT 0. ~3t"(§JU. 13 Muth C26) rt-JfjJJJij(.mJ~ , ~~fJH~~it- :.l~~ITl¥lU'LtfJt£~mt;;K5!fl~ ~[)PJT~~M:ffij~~:;r.;-I*. '. :tmReid ( 28). , Lee C18) ' Winged 36 ) 'DeLeeuw ( 5 ). • :RilJ Polinsky ( 27 ) ::tfl-il~ - ~ A ~~ft9~ gffB lWI~it*4. 0. Polinsky~~~Ulftfl~. macro data vs micro data ) (f.J ftS~~~fi'fl~Im . ' @;;k¥Xffij ~~ IT. ll\t{itWJj~ZpJT~Nlfg;:tEo. 81i1i '. 1'I:f4HlllM:tE -0. 75 • rm m~1~Hlijm~tt~~1Lt. ffiib'l&~~iB,RJi!51}jllittiElflm~ (owner -occupied. housing ). 0. ~. , f§..m~ ( rental. housing) 1JIlQ1.1t511 .• ;r;Wi~I'jfiHmm~Zra' B1 !H1~ 0Shelton ( 32 ) §j~ lttU5{± ~ra'~[OJ~. , E!flm~1lf,"U1Jzrdjft9Ilt*~J~. Henderson and Ioannides ( 12 J. 0. W& Rosen and Rosen C29 J ,. , Rosen, Rosen and Holtz -Eukin ( 30 J ZfI\5t. ~ IJ ~~~tE~l5J t~i5t~ EI~m~1!f~m1t1!ft£i1!t:m-{1~IJ:tfB1J£*~~. 0. McFadden C22, 23, 24Jt£~m3O{ffl~;;k~m ( the hypothesis of random utility maximization) r • tlHJtJj~j!UI~1'~ ( discrete choice model). 0. ffii. fil.Jt~~.iE£-flJn1:~Hr.]I1j1iM:~~. ( qualitative choice) ,1R:Ji!~~~1JQ,(jlt f~. ~f6ff A~~filJtm~z~ifi • jmLi. C20 J , Le~ and Trost (17) , King (16 J ,. Boehm ( 2 J. ,. Gillingham and Hagemann ( 9 J 'fDSwan ( 34. ~~~~~mH~~~·tt~~~~~~ {± ~~. J o!J\ ffij. , ~~. ~~' ~ ~~~ f± ~mft Z ~*.

(3) - 139 . ( ~O~~ ) :Jl!:mii"illtiH1 ~~~i'M~*llSlllt5i~lltll;tt/5Ji*~ ( joint decision) 0. a-:J1I!~~. • tzOKent ( 15 J ftIIoannides and Henderson ( 14 ). 0. Ijllit-t~ fJU:t~~ W {!l~Hm* l't-Jx~cp • :m:*~fMJJit:£~Z -1m!r£~I't-JOOHff ~~~. ( microfoundation ) *m.)dr'tzD{iiJ{£m~~~ , {±~m JtW;ft ft!!.M1;!fm. JIlzra~1IitX!±lft-J7HicQJ.jI~jJt5&1:IHj*. ml±llr-J~mt£5HJj- 1J i'! itfj~ l~ ( ~*I~t£. 0. Ellickson ( 5, 6, 7 ) i*Jf.lRosen ( 31 ). ( hedonic approach) • 5HfT A 1'rJ{fiiitt -1m ~1JJ ?t~J ft-J. indivisible commodity) (characteristics ). 'tm fiiJ it~lJjtS&JfHj*. @')z~IJJ7t~Jft-Jrm~. ,. 0. ffiim~iE~ - ·@L.~,fi. ~ffii?ttR~@m.~t£tzofii1~. m~m~&tzofiiJ~.A~mmm~~ - @~~~~e o. - @~~.~~ ¢ ~*.~I't-J~a~~fim.m~W~fi~~z.~t£ou. r 20). mtl:lq~~ ~. ~:g)(;.~. ,. ( the switch model ). , ft!!JtJ2).- ~LogitffiSiJtx*~?t AfWl. ~~9}.7tlJIJJ2).PNfl*/j fi~~ITm'§!D~~.~ff~. ltm1?ltt~t£z~ f.a;. 0. ,. mftg ~~~W~jifq. ~ffij '!lI;~~~~~~rr!¥ltli§M{±~~.J:lH&~{iiJ. ,. ~fl. mmRMM ft-J M~~n~ o ~ ffii m .m.fi.Wm.~~fi~.ttt£'~~ITA~. ffl.~M.zm*~ ' ~~~~~Mz.~~ '~fiz~~M~.l±Im~~ o. (=J fiJf~n~. :.$: JGtit.lr.-00 ~ IT ljl!. til ~ Jf.l tii* {t ~ ~ , ;tt cp /5J ~:f51.11± ~ Ir-J ~ IlJ ?t~J M:R fJn~' ~~. 0. Jlt-@Bff~~~f.f~8Jl ~tI-¥UL tzO{iiJH {i!'£ ft-J ~t£*.Elg.. ( combination. of housing attributes ) R ~ ft!! m .M1izrdH~:m:f!m~ , ~/5J,*~l~HHi!~~ ni±~~.. · ~tfH!5 ITm JVI~zra~~~fiftt ' *~'f* ffl McFadden ( 24 J tlH:!Hf.]i!. itLogitt~~. tt-.Jtt~t!t. '. ( a nested logit model) ';1t.fl!.Ii~@H~fiJ£I.]I:ti{~rrm!l~~. -tl!Q] .I2J. /5J,* ~~-~Logitm ~ JiJTIlJ~I'fJ~~. independence from irrelevant alternatives, ITA ). ,. l!J1mrHJiI~rt-JI1 .lr tt. (. 0. ~~:.$:~M.~tt ~ *t£tzo{iiJ • • A~Ir-J~.,~~.tzo~~~~cp~~*rr.

(4) - 140 . .IiflUi: ~¥-f ft -tzD foJ ~ ( a reversed. ;rt: fil. .m: ~ m. ' iN l!t 1rHt li pfr ~~ ~. nested logit model). 0. "&!cJ Jf! :i:\':Logitf~ ~ ... Izlffii~ W~{t~t~m5i ~~~ McFadden C 24 J. pfr ~~ ~ R~ ms& m ~ IX ' ffi] £ Ellickson [ 7 J pfr f~ tn [)~ M~ It (the bid price function). ~i\e. (tt- ). 0. *X~ =mjfijJ£Hlt~ijH& m :@E*1 t sl:km~ []Lj 1Rl?iittz-tt!t~±. ~t~ ~-~1t,. X~-=-fnll'~ {i5 ~t ~"t*ffl ~lJiP: Logitfe ( a multi. nomial logit model) , ffi] ~£~*1C aq =Jj :l= Logi~~ ~ :i:\':~m ~ "&[PHIHW. ~ Jl:l= lo gitfl ~". model). 0. tE{5 gt ~. 0. ~ffii *X"t*ffl l'8 {t gH~. ( a reversed nes ted multinom ial logit. , *X "t* ffl ~~ii*~ {fJ. {t5tt ~ (a seq uential maximum. likelihook method) 31dt ~t J: iillf~ ~. 0. iiM*. 13 ~lI tt'B ~JlHm (Departmen t. of Housing and Urban Development ,HUD ) Jfj-~~tt=E~WJ~~ Assistance Supply Ex eriment, RASE ). 0. ;lt tj:rfr~m~M. '. ~. (Hou ing. -*U13~ ~Green. Bay, Wisconsin' ~ - m*13 outh Bend, Indiana, ~ mlWTffij ii 'M ( crosssectiona l dat ). 0. •. 1N ~1:!tJ:}Jft" mf.!utJt ffij{±=EIlJ5t~fil.ijfl~ ~. ' f±=EHlSz~g.M ~x ( a composite commodity). 0. : 13f;Jm ~ (owner-occupied housing) ~fi1. :tt m~ ( rental. housing) , 5t~IJj2.H, j~m~ , M1!fft"Ilm ' f&1!fff Jlm' iNi=l, .. .... ,I ; j= I +l. ··· ...• I + J . fI}ffli ~ ;ttffKfI{t=E!f-fftZ ( housing attributes) ,:ft1M 0K {i\t1~~ ft~fl m~ J §. ZI = ( Zlk ). , § $ 13ff~fil. 1t.1t Jfj-$ff Z~t! Z,ilJ =. (. ZII, ······ ,Zlk ). ~. 0. iN.

(5) - 141 . ffiM-m-=ftliHtUt-JmU% (housing service) ffi*~t1z m~JiJT#(J:E. 31H3:!t JiJT Jt ~tt-]!f,Jt!. '. tt m~ z {fi ~W'. El ~m~!'Mm~m ~{I~ fOJ :lb}~IJ~. 0. h = ( hi ( ZI ) , ...... , hi ( Z, ) . r = ( rJ+I ( ZI+I, ) ...... ,r,+J ( ZI+J) ) . mt£{gj~~~t¥tL.tt-]~ffli?jgJl~~Cobb-Douglasm!:@. , fffi:!trp El ~. mf£WflHt. m ~ m*::r-I6JZ~~a, b ( tt=) -1WJJt!m!tt-]*~1¥itL.Wfirnil~~rF7iJ~,)(ffl ti* {tro~ Jf1i : 0. Max { x ' Zi ' Zj }. (ll. x' ( az,+ bZ j ). s.t. x+d(q) ( h,(z,) ,······hl(zl) ,n+l(zHI ) ,······,rHJ(zHJ)J = y. J: Arpx~~U~M. ( a numeraire). 'tt;tt{l~~l. 0. y£*~t¥tL.JiJT~. Delta~It' l ~ q ~ I+J. =;fi*IT¥tL.~1f mql1tm~~ 7C~~1. :!t{tB~O. '. , JtIJd (q). 0. d(q). ~. rpfl ~ tt-]~q@. 0. lZSlI.M~ 1W*gt¥tL..R~;I~H±t£-@tt!!1J. '. tt{ih::r-~m~~~m~. ,. @ ~ ~gliN. ~~~o~fffit£~#(J:Ett~m . ~~,~~~~#(~;ttm~~momM-m ~ tt-]~~. 4~t!XJt~~PJ 5t~J. ~iJt1P'.f.t1*m f!.lH1Ett-]Lagrangian1Jf.t 3Io.l<J:.;;lli *~t ¥tL.. '. :t:')(ffl~*tt-]:§:f1M J1tJin;~fr'iJ Iffl-@=Zf7ft1Jf.t ( a two-step process) 0. (tt=.). o.~t£~~tt~*.z~'*ili~~¥tL.~~~~ . # . ~md m ~~m'B~;tt ~')(ffl~*. '. ~frr~iJBffl rJH~~ffl~~. (indirect utility function ) *~;ttM. 0. =;fi~. ~~.tL.~m~mm~m~'~;ttM.~ffl~ . PJ.~~o. (2). V,=V,(y - h;' z;) = ( y-h, Jaz,' if d=d(i ) 'i=l, ··· ···r.. =;fi{tB~1fmmH*m~. (3). ,. Jtlj:!trJ1.~mm~~Vj ,. Vj= Vj ( y - rj , Zj ) = ( y - rj J bZ j , if d = d (j) • j = 1+ 1, ...... ,1+ J ..

(6) - 142 . (4). ~~¥ ro: pJT ~1fI¥J*ji. ( household characteristics ). ~JE7 t±IHLlinora1:Ji5& m~Iic tU~. ,. .fie;ltrdj:Ji5&m~*. 0. 0. ' m-= tJ7ft1£* I±i ~IT ¥ ULt± ~ ~f~UR~ IY-J:fi. tt .~:lfffij. g , ~m~~jiiHt:*II:&Wfi'lJt't ( 2)flzu.~7t. ffii~,ftll. fJ. v; I. (5). -. (6). -fJ VI I. fJ. Z~. fJZjk. y = yo. =. y =yo. fJ V; fJ hi - . fJ y fJ z..... for all k= 1··· K.. -. = -fJ Vi - -fJ rj . fJy. for all. fJZJlo. k =l···K.. ff(llt-=tJ7U 1Jji , ft 1'P'l"i'lJ £{ FoJ Mt~ !ilmJViJ~w t± fAH~~ · ffij 00 ~ ~t . tiL:s& mtilli:*: 1t~.zlittWi1~tJj. 0. ~rdlii~m i§lt.pt.! iIiiilt. :(£~~~W~~'M:ji:s&m~Iic~~lli~~~~.~~~~m ~* ji .~~~ ~. , ~DfiiJ~.jJJ)drHl?lIi:s&mtr.J *~. 0. -(t:(!4"{MW~~. I. ~- rt2~eag j3- 1ID fllriiJ& ~. *~·aM.~~~.z~ , .~~.tiL~~~ ~-OO~~M.~' D. :Pit ( willingness-to -pay ) I{igflit~;lI\-H.wglic. ( bid. •• D~M.. ::t iiJ £{f*1t~ $5( m ~1! ? Jlt -'&»IWTIl:fi M ~~ 1l£ B':J. price function ). 0. $. J:., $5(miiIU~ IU-l .ii.1£.~. J:.~m&l¥J=oo.~.*wg. o ~~~ Bm ~~¥tiL tr.J~~r '~.~~R~ fflm.

(7) - 143 If."f~tt-]m~. ; f&~JlIJ£t£jlfif;tk~*f{lr-Jmf?l • iHBlfiiJf!i~~~U'1.fiJT.fl. ~fflr-J~ • • ~.OO~~*. ••. 0. M~pJT. *~Bm ; .fi~.~ ~ .~.~.mfi~~ -.. tt~*~~M~~M~~I~~~~~§'~~~.tt~~~m*~~m~~m~. ; W~~~*~ ' ~~~~~~~~~A~ti-~m~~D~~M~~~.m~~ ~o~~·~m~~~. •• ~ffl~~~~'@§~~.~~ o. ;fIJmtt~ti~ ( duality property) (~~) (7). I. :ft.fl!~P]~(2)J:\;i;\(~~. V.(y-<I>r(ZI) ,Z,) = (y-<I>.(z.) )azr=: u .. ;It ¢ <I> Illf] ~Ht • {j'[ I.i~d±: ~ i ~ Ill-/ 11 ~.. ' ffii u~ ~ ffl7k*. W~IJ ffllliH~i5i fj: JE !.I. 0. ( implicit function theorem) ' ViJ. (S). 8<I>,(Zr). 8 ZJJ... V,/ aZrk. oV,/8z". f). 8 YI/ ocD.. 8 Vl/. ffii (8) A~(5)A ~~mFcJ • ~7QJ.~1ll~mcD,nttuHa~$:h,. ."1'" · ~m. ·. (5)~IDiBj}t£3j(5&)fHi*~. I. for aU k=l ···K.. oy 0. ~~rtlUi-e~~Jt{± q:; ~jtZa]I!Ij 1t 3A~. (8.rJ. ( y- hi) ) / az; • ~"*;lt~~{1~ ( hedonic price ) , 0 h;/. iffi(8) AftIJ.~~ff.tiL~7m.{t~~~zn.§T.IUii~MrfJ.. '. a cD ;/. aZr.. 0. OZlk' \t~. ~~~*~~~mo~~ · ~~~~m~r· tt ~~W~m~~m·~~~~m '0~~~~~ff~{j'[~• •~MIr-J~ o k~~*~~~P]~~~~.{j'[~m ~ ~ Lfi~. , ~~~fil'jWiUIii. I. Mc~L. 0. ='li~~fi H&rEiJ.~~IJi~Loigt.~. 11~U!itff~flT~~~1fltlI. teristics) , tET Mil~r.Jl1U~ .. 0. :pJ~WiW. <1>. (ZI). 0. • .~*!t.tiLt~~;r-;IClr.J~~ ( household charac I. 1ll-/{li?iiIt~~ff".tiLt~~JUt - f!t m~ i~B~ ~. ~t!i[tJt~1t. • ~fJ11~H~ff:tE~ ~ JI!.Jj (ra.ndom.

(8) - 144 term) e". 0. ~ lIfl J:*~ , m-;ftm §[i!\~il 'fJUlh~3Zftiii~1I41Jl {g :mB'-J ~~t ¥. tiL fiJT ~ {± , lZSl ffij fl ~;ft m ~ iffij § ,. 5: ~ Z 114 M ~ fA <!> : ffI ?iIi!i .@ --f;~t. tm ~~-IM1l51~e ·dj ~;fEj[OJJ3.lBi.litJj.lLsqWeibu ll5t§C( an. , ~IJ(9)A* Itt-@ILogitf~~. (HE). 0. :. iid Weibull distribution). ffij~;ft {± 'f:;i1&:~ §t ¥ tiLtfiJT{ti~Jt sq #;l $~aD. :a:~a2)A '. al). petl z,)=prob{ <!> ,(Zi) + e',) = <!>"(zi)+e',,, t"Tt', t, t'ET}.. 02). P ( t I Z.) = pro b { e ', - e'" ~ <!> ,I (Zi ) - <t>, (Z.) , t "T t' • t ' t' E T} .. a3). p( t. I z,) =. exp ( <!>, ( Zi) ) t/ ~ T exp C <1>" ( Zi) ). a3)Af*'-fl~HI1i~sqLogitm~ , .:t~~.'M{'* ~B- T !t{tHmt ~ ~ Ii .lL ( indepen-. dence from irrelevant alternative, IIA) -:e.. W~. (at). 0. ~~ ~.lit-lE tI ' -ili. [OJ1I'!j:~TlIHH5§tfJ11tm~~E1~m ~ ra~sqt\:~tt ' :fldFll*fflMcFadden C 23 ). #ttBz m J:\Logitm~ ( nested logit model) *~iEa3)A'. ( 2:1:/\). m. 0. ~-IM1l51~~-lI~li ((.] m~LogitmJ:\ ( two levels of nesting) ,ft~-Ti. 0m.m~'~-~ 0mn.m~o~.# ~.tt.'-.A~tt'f:;tt.ZB'-Jm~1&: *~¥tiL~~~g((.]~$~~ .~ '@~A:. a4). p ( t..n I Zi ) = P ( tmn. I t., • z, ). 'p ( tm. I Zi) ..

(9) - 145 . , ;It;!!$~-IrnI ~ Ij~nX;-1WIfitj¥I¥1Logj tmm ' McFadden. ffij:tE-Rt - ~ ft-] :m~~~cp. ( 1981, 1984 ) ~ Bfi~1!I Et-J :#.l1¥IJ5~~ : (I 5). p (. t.o. I 1"" ' Z; ) =. exp ( ax.." ). tM~ E i If. exp (. (6). p ( tm I z). =. (7). Im = log (. L. aXmo' ). '. exp ( bx.. + dmim) tn, ~E T exp ( bx.. ' + dm' 1M' ) ,. tm", Etm. exp ( bx."., ' ). J'. :j{:CPlm ~~ m fSB9~ii ( inclusive value ) , X.. , Xm.ftlj~~ lm ~~[U~H\l.pJT ~~Et-J ~'ft. '. tm ' tmn ftIJ £=1'lmll~ [t ft!Urzm~. ,. t...nEt..E T ' a' b ' d~~Jl(. 0. .g. L~ ~m~.w~~~,a.~~ - . ~ ~~~.fl.m.~Zm ft-] .. ~. 0. ;fi~~WLrB'£7¥;~!ll:rr.r.J. Jtt fti!.Log i~M. ftlJmmit:W McFadden. model ) ~ - ~ Cinconsistent ) ~rJJa. l~rfmJ;t! ~Logitt~~tWl~{t. ; ~~iJj.L rJl1iJ7'G~~ft ( perfect substitute) ,. fI~~O,;S:<L.,*~l'. cation ). ; ftIJ Jt~f{d..1f~~l '. ' &HJi1J[JJa~ jf.. ( tt:tL ) ,. I¥1 I!jfHIS&fIH§;! ~. ~IJ ;it~fidm. ( random utility. HIJ~~~1rrlm~ r.J ~ .ll: (model specifi . 0. m~ ~~-~.~*~Rffl Et-J :m~~~ o .1rrl~~M-.m~e.~~.[t. m~ ~tt '~ M~~.~~[t.mzm n '~~.~ mzMlJDWm~. 0. :!1j71-.1rrlA-~~I:1e~'. Logitm~.*:4~m:i!.. *S~ cp. •• ~&~~, ~ mfi.~.. re-mmtt-.J=u. ( binomial choice ). ( multinomial ) tt-.JLogit• .~. • J: 1S QH~f;fm ~fDfil:a: m ~1!fIll7t '. 0. t£. 1'r~ {Jf ffl ft-] =~m J:t. rJSft IJ0 .rrl'iHrrz pJT ~Ill71fflH~l&. A1!f '~ M~ A1!f , ~. ~.~ o ~M.~~ H :. H••ft.Ur ~ ~.W71-~f;f~~a~. tt ,m.1rrl.:4 ~ .~~~ ~ ~ tt ~ ~*:wm H~Wz.±~~~'~0Z~A~&~~~~ o®L~Wtt~~~~ 7t ~ .A~ ~*~~ ~.§~'~~m~~~~~ Z M~ft~~ o a7.~:W ~ ~J:.

(10) - 146. :ffHtm~~. ~ r:pJ&A. ~l& A. tj:Il&A. ~l&A. 1l£J&A. ~.!ff. ~lil. ~~. ~. ~M1t '~6M*~±~'~~~~~-@~ J:!i~U~fI~n:UJQ{~m1lifJ(f.J ~. ft.. 0. ~J&A. ~. J£!;. ~. ~. Ie;. ~~~~o@*~~~ff~~~' ?:&rm;ttm~~ft!i~ J:~1jUtH~~~. ~. ft!i~;g-PJT~M!{j~o. (::)jn4~EI~. *::>c ilE ffl t'fJ if *4 * E1 ~ i/-lJi ~ ~ @ IV) 1#i ~. Supply Experiment, RASE). 0. ~. ~El3~H~i±~&~nHil1i$. Q{1974£F.~ :!!iH!!1f:. ( Department. !±Iii • El3}J[j1tiRand~:g'J$t. of Housing and Urban Devleopmel, RUD) tiliDUALbas.~~. ~ (Housing Assisi lance. (Dala Use and Access Laboratories ) l&mi1:fZ~E!jffl. 0. ;It~i!U' .::. if:. . MrJl;g-:fI}lf(@1!1j. ~~~£F.m~~i±~~~~~ot£~ - @~~~,~~rr~lJi~~:m~~. , i±~~~ , :&m'@NM~. ~1!1t1IDQI.~8A. '. ( 21 ) zmiB~BJ.I. 0. ( base year ) • HASE{£Wisconisn1tie9Green BayfOTndiana. 1tI ((.JSouth Bend?t~fJ VirCl~~~~2500P~:?t:~. m~1.1I~. •~. 0. ~$;J;t;~oo~2ooo@lrCl'~. 0. lttltlt~ir'.Rti~m. tiHASEJf~*jfft!R1:.@~m1filJ QI.~:;-Boren 0. m~~~~*~m~mz~It1IDQ{~.~r :. ,. ( 3) fOLowry.

(11) - 147 . AGE :. ~.ft;t~~. tf~!!HI~JH. EDU :. ~~;t§r:~j(i~;iFr&. ,. • l'llj£)j(U~$. 0. , ;e:~mJl~Il!' l'llj£{S(U~~. ( year of schooling). 0. mCOME : • • ~~·~~m~.*~.mM.~~;t.m~m~,~~m~.. ( discrete variabLe ). JTX~-.i!t!.~~. <). JZ. El31fi:'~z~N';H;tl1f~'UH~ . 13t. ~R~¥~~~m~~'~~~~rr~~~~~§~ffi~~~o~ ~.~Ii.~ -g. ,. ~1&i\tEO~7.999~~Z~m;. JNCOlV1E = l : COME=2;. ~J&i\:(£8,OOO3~.J3,999~~~. ~J&i\:(£14.000~~£U:~. ,. 7Ij~{tI.;p.JH~~ ; TIfz~;tt. , I. ~lj~ rjJPJTf{J~. ~Jj ~j!$PJT~:lli-. ,. , :itIN. illz~:itIN . COME = 3 {ttijlttmli.:M § • ~J&i\{£O~6,999~j(;~{ttPJT~1tf ' :it <). INCOME = l ; 1¥J&i\:(£7 , OOO~l1,999~~~r:ppJT~~' ;ttINCOME= 2 ; iFJ&A:(£12.000~7C£{J:~~pJTw.1tf • :jt INCOME = 3 KIDS :. 1 8~£J.~/j\~Ij(§. 0. 0. QHOUSE : ft~i±~£~01m~ • ?t1!Jj9 0 ;e:£'liiHt • ,QljQHOUSE= 9 ; Jllj ;E~ j[fN~. • QOUSE= 1 0. QNBHD1 : tt~ ~f1~P.JTtEZ~tU1Mf~!rt-J.g~m. ' ~111¥1J400 ftlJQNBHDl = 111 ;. Jtlj;fi£'I1~fi. ' QNBHD1 = 400. 0. ;E.g~~NH- '. 0. QNBHD2 : ft~~~.g'fll't-Jmlt ' ?tUlj9 tf£l!1Jf ' QNBHD2=9 ; Jtlj ;fi£'It~ 0. , QNBHD2 = 1 RACE : ~~fi1m. '. RENT : wF.l /jHil. 0. ROOMS : SIZE :. 0. ;£f~B A. ' JlljRACE = l ; :6'~F8A · l'lljRACE = 2 0. ~m~pq;tmra~.§. ~.ADm:§. 0. 0. SruOURN:.MMM,~mf1~~i±.~z~M.m, £{ ~~~~ o. TAX :. frtimffl. 0. TENURE : 1±~m~. , ;fi~ Elfifmli. ' JlljTENURE=2 ; =5='AHJll!mEl'. nlJ.

(12) -148 . TENURE = 1.. ,. ~~~~~~~. ~ ~ * H, ft~~~ili§~m~~~~~ • • &·. O#!t$ , IJ\ ~$ , .~£F~~~. ra' ' SOJO RN'. ~~JR*~Jlt. ••A. sA$3($ • iffil3~m~~Wfil1tm~1l~1i1i~ 1flf. ' M:?!,lJiJt]~l661mJ.l fo1 79 J.l. ~1!f.R ff361mJ1 fo. 3700 ~ • §~m~~mra'Jt§.m1t~$. · .§.£lU~W'. fIH~ ~. Bend~r~. , ~ 1k~@~Green Bayf OSouth. ~~m~WW.g: A ~][fl:. , ~1J ~:m'ii*fFJ~Jt • :*~ffii. § • Green Bayf* - @ JJ.lZ~CPtr-J~ m ' m #U~$ rnIfF;fI~ , :k~$~ fJf ~z.zp ~1& A.~. , South. Be n dJlIJ A - @~~~~m. ~~~~i@j , t6czp~pJT m~ f'!£ ~ o. ' I5JIflfGreen. • Green. Bay~m--1-ilBt:* ' JiJW~~. ' AO:ff 70fF. ft~l!WT~j> ,. Bay~~* ~ *. A 0 ' 5HI#!t § ~. , ~~iF~.~ , ~ - ~M~~.AGreen 1Il~. ' i!&J5. Bay rn~ Alt f9IJit.So uth. , t6cltmtil;& M iln~?Jl' iIBt:~. Bend11\:. 0. maximum li kelihood method ) ~ ~ -:!x Wi :* lI!l ~ ~ (sequential maximum likelihood method ). ( &1+). . ~!t .ltJf.' :Jt$~ iffi ~. 0. M1!f5&~f1fi~ , {[IT WJJ.lZ*i!t~ , ffij '§' fr J4! ~*5. , Jtmtfl;H!i.<ilt:ff~~~~[!!]Qf.J ( concave ) ,ttfi5tt .1. ~ it€. ~-~m. (&+-)o *~.ffl .-:!X.*.G~~~ · ~~~~~.3a '~ ~ IT z 1*f,{~- f,{f! (consistency ). , ?#Hn~~ tl t~H'!£ f5~t Z.~~~. 0. tt '. @:± ~8lUligt~1r!l~t~ J!~ln~* a'-J ~.l!£. ~ f5gtm'i ' ~1l'~ 9Gfi5~tJfHW*E~ cpa'-J~lI. ' t;RfflQ5)Jt . ¥r] 0. • ~A$~-(5g-r1i1t Q7JJ:\Q.I. ~ .~!i!z~m ( incl us ive val ue , IV). , M~:pJQ.I.~~¥IJ ~ ~.~ AQ6)J:\ ' (t(.1.~ ~=-.x m. . :fJt1'Fl ~ ffl SAS. itlZQJ. ~ fr5 rr M!~~:mZJ:.Ji. 0. : MLOGIT~~ flHASEii*4 cpi't:J m @l ~ m. Green BayfoSouth Bend5t}jIJ WI£.H5IT ' *6*7lJM-*(::) , ~S '. & ~HI!!I) :tE 5ttfdr5. rr*6.~M·aM~m~.~~Mfi~~~~~ ~ ~~ o {t(~~~ ~z M~' ~.

(13) tt : ~!\!f{zl¥l tJ:~~~ iEX. 0. 4.115 6.805 151.267 6.334. 36.443. 12.208 1.041 1.000 114.317. 35.065. 1.894 2.394 0.683. mit. Green Bay. 7.336 136 .293 6.534. 483.514 166.480 5.627. 11.330 1.009 2.000. 3.486 1.345 49.043. 1.968. §~. Green Bayffi!.South Bend a9. " if N~a}j " -ffj z~ a}j. 6 .911 148 .275 6.374. QHOUSE QNBHD1 QNBHD2. SOJOUR N ( 62.427 4.417. 1.200. J3 ) ROOMS ( Fa' ). T AX (US$). TEUN RE RENT (US$). KIDS (A ) AG E ( ~ ) EDU (1f ) RACE 12.032 1.035. 1.909 2.613 0 .815 37.858. INCOME. SIZE ( A ). ~H. ~fi :gm. *(-). 5.286 7.278 121.936 6.457. 36 .824 4.l05 6 .33 7 125 .942 6. 2 17 4.362 6 .542 125.070 6 .270. 1. 134 2.000. 10.552. 0.906 51.021. 1.8 10 2.861. §f:i. 67.867. 0 .732 36 .900 12.051 1.208 1.000 107.816 - .., -. 1. 8 18 2.3 29. ~ll !l. South Bend. 231.011 179.422. 1.192 2.218. 39.974 11.812. 0 .770. 1.817 2.445. ~M. **- f-#. .j;>.. I. '. .{).

(14) - 150 ~(.::.). T "$t~ : Green. Bay, Wisconsin. 1Jtm"J!IX: INCOME l3~m~1!f. M"~1t CONSTANT RACE EDU. AGE SlZE. KIDS ROOMS QHOUSE QNBHDI QNBHD2 TAX RENT fI~~. LOG :If€HI;.Hitl. x2 EHiJ £. {g;1&A~1i1. 8.720 7 (0.0505) 1.01 34 (0.0059) - 0.2848*'" (-3.4278) 0.0630** (3 .6981) -2.6048** (-7 .0402) 2.6687** (6.7483) - 0.2606 (-1.4516) -0.1358 (-0.7289) 0.0076 0.3951) -0.0924 (- 0.28 78) -0.0048** ( - 3. 1490). CP1&A~~. .m'ftm~1!f flt!&A~A!. t\J1&A~1i!. - 0.5248 9.1686** 3 .7 192** (-0.041) (7 .9533) (3.6908) 0.3427 9.8292 0.1905 (0.0772) (0.4943) (0.9915) -0.0964 -0.2471 ** -0.1071 ** (-1 .6460) (-7.8055) (-3.8158) - 0.0050 0.0049 - 0.1084** (- 0.4118) (1.0060) (- 2.3123) -0.7309** - 1.4262** -0.45" 1** (-3.5607) (-1 1.3 2 19) (-4.4259) 0.7645** 1.3320** 0.4526** (4.4259) (3.4473) (9.7441 ) -0.3308** -0.2212** - 0.0708 (-2.5419) ( - 0.9350) ~ -2.6160) -0.2477** 0.0690 0.0680 (0.5111 ) ( 1.2657) (-4.1248) 0.0048** 0.1729 0.0052** (2.9933) (0.3578) (~-3702) - 0.3319 - 0. 2 104** -0.0225 (-0. 2118) ( - 1.4672) (- 1.7160) -0.4172** (-4.0470) -0.0045** --0.0061 ** (- 1.8358) ( - 2.8144) 440 1762 -303.3324 -1665.7463 47 7. 1291** 310.1424 20 20.

(15) - 15 1 . * (;.) r Jf $:li. : South Bend, Indiana lltMf'J!. : INCOME §J~m:m%. lIt1&A.~~. M~f!lt. CONSTANT. 4.2841 (1 .2040) 0.14 16 (0.2274) -0.24 58** (-3.5025) 0.0588** (4.2370 ) -1.4345** (- 5.3298) 1.4947** (4.61 07) - 0.3051 * (- 1.8877 ) - 0.2 203 ( - 1.5440) 0.0189 (0.8341 ) - 0.0569. RACE EDU. AGE SIZE. KIDS ROOMS QHOUSE QNBHD] QNBHD2. (-O . ~ 108). TAX. -0.0014 (-1.2661 ). cp1&A~1H. 8.93] 1 (1.2042) - 0.1024 ( - 0.1932) - 0.1078** (-1.7612) 0.0053 (0.4483) - 0.6641 ** (-3.3878) 0.7259** (3.0523) -0.1003 (-0.7722) -0.1681 (-l.3 71 8) 0.0139 (0.6318 ) - 0.0413 ( - 0.1775) - 0.00003 (-0 .2 168). RENT 1il~1~. LOG. 374 -300.5538 170.6776** 20. ~@'fiQ:. x2 rB~. r>tm : t.5~r:Jf.4t- ~ ff '" 0. m1tm~~ fIt'!&A~rg. 6. 22 59** (4.63 74) 0.77 3 1** (2.9484) - 0.145 7** (- 5.207 1) 0.0 128** (2.3699) - 1.1 458** (- 8.1070) 1.1 762** (7 .18Q2) -0 .2437** (-3.1035 ) -0.1611** (-2.6553) 0.0136* (2.0272 ) - 0.31 32* * (- 2.5549). cp'!&A~1f!. 3 .76 54** (3.6908) 0.6033 (2.5398) - 0.0970** (- 3.6835 ) - 0.0135* * ( - 2.5057) - 0.592 7** (-4 .8483) 0.6061 ** (4.2069 - 0.203 4** (-2. 7865 ) -0.1073* (-1 .8597) 0.015 7* (2.401 7) - 0.1 863 (- 1.6 19 1). - 0.0026 -0.0091 ** (- 1.3927) (-4.2918) 1344 -1249 .4 123 354 .205 1** 20. * ~ * ~ , 51/jIJ~~tEl %~%::t~tf7l<$T ' m{:fifiWi~ft:J.~o •.

(16) - 152 . At (\?3). J:. Ji ~t ~. M"'~1t CONSTANT. SOJOURN IV fi~f®:. LOG LIKEHOOD. x2. ". Bay$!.South Bend. Green Bay. South Bend. EI*f m~1:f. fJHtm~%f. 0.67 18** (3.5841). l.7 804** (7.7 359). -0.01 23* * (-17.150 1). -0.0 132** ( 16.0588). 0.9043** (9 .0276). 0 .275 9** (2 .6388). 2 102. 1718. - 80 5.227 1. -668 .1407. 592.2086 **. 464 .0970* *. .. EHs JJt. malized ). : Green. 2. 2. a'-J£1I 'H:T !;jp)Tfij, A1{trr tP'. :ft{Fj Ql.~PJTfi~ ~~~ ifi. '. ~ ;tt 1t!!~. ~~ITZ~ ~~m~~ ~~~~~ ~~~ * ~Z ~ OL ~ ~ tt~~ ~tP ,ft {Fj Ql. EI. ~m~~~~~' ~~~Z~~ ~m .m~~W El~m~1:f z~o ~~~~-m~ ~1E~lIjH~l t6m fi!iIT~ tP. ' m$~~If1!~f,! § ~f1!{; ~ it ?'I.\rmrnM-*),(rM{t7 0. .MM~,~m -@m~ ~ ~~. m ~.' ~A o ~ § ,~*~~. ,. ~ ~ltm.J!1t. 0. F.i!N lttmH~z ?&t ~ ,~ ~,~. ~f{·Ql.iIi£j>~ITL Z~1t o. {i5~tAfr-J lE6ttt. 0. ••~. ~ltt ~l#fZ JIJ~fW*,.

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(19) - 155 HA~."~M~'.~~ffl~~.tt.~~~m~~~~' ~ _~~~~ffm. mmWI3~m~~MM: '~1'Flijt1L7PJT~~I't-.1&[o]M!~$JiitLog i t,fl~ {~~*)( (f:JIT.li'H~~. 0. mHASE.*4cpI¥11974£F~[o]iU~. Bend7)- JjIJ1Jrl Ql. ~ g-j- , ~J(AFl1l ~~M:. '. ,. fl~IIB~~illJjnpGreen Bay~South. mtE ~ 1i:lJ£rfi'i" tP ' jllHt m1!i~ 1m !Y-J lit ~ ~ ~ m.lt ~. B~;ft*/J\~~rfiffij~. ~ffii. 0. ' 7ttT{t;ei'JUt~ , ~~~~IHltt;e~rs'l'fJ. ~~M:'fr~f!(fiSg-j-J:89~~o. *)(tE~tT~fiS~J:~@.~,~.a~. JfH~~ii*~{jjlfiS~m. '. ;ft~I6t£:1n$&~~{lt. •• - ~m~89.~:. - '*)(R ~~ffl:ft?H}t~l&*~tl!H~m. 0. '. *S~. ~~~~A~~ o .='*)(~ffll¥1~~*S~~~~~·m~~~~~~ .~~~ ~lI1tt~j!tii-zp7}-J&5!lJT. _t;fEfiS~J:M~~ ••. · tm~;!AD.§*/J\. 0. rJ]~IH~:iiM!~~~;fE =: Ri Ql.. t •• *SI¥1A~mA~~m~.smfiS~ o .=:,.. L'~7tt~~.~tt~~~~~~Ql.~'~~~M~ft~~~~~~_~~ o. ~~~ffl~~ -1i.~t~_m~J:~Rft.ml'fJ=:~~~·. •• ~. - ~m~1¥1. ~[o] o. it. Iij;f. [ tt- ) ~.mDljfili:5ttife:J!j!~rSJ'R : Lerman and Kern ( 19 ) , Gross (10 J , Horowitz ( 13 ) . (tt= ) a, b~~~~~"i"¥t'LZH ( bousehold cbaracteristics ) · 1it'l'rq]*tt.u{ilJfi~~lI'lJza, b 0. ( tt:=. J JIU!II.::.-tlrg:ntl;7"JJ!~.1t:!:Re:J!II!ffl c PJ#J!Rosen ( 31 J &Ellickson ( 7) (tt~) ~~Varian. ( ttli). ( 35) • ~=:•. 89~92J{ ~;j§rq] . EIJI:lL~7.l'~. ."L.R I! e O.. 0. 0. ( iid ) ' 'I'Ga'E;.1t Wei bull , normal , logno rmal,. exponential. ~IDogistic7tlk!' r/>, ( 1.4 ) «!5i1'1f3I£Logit5tre ~fc1iYQ McFadden ( 24 ) YQ1i. 0. tf1lirutDt;f.\Weibu1l7.l'~:::f'fqj. 0. ~~M. rp , Cz;) JJ~~*I3-00:mIl'lJ.§JJ\lJL7.l'~rp z .*fil. Cmaximum value from an iid ) • ~ffii~, ( z; ) !3f!~Iilt-@tifil5tnc (extreme value distri bution) , 63~65~. a. 1it1t:e:*.J:Uffl1itLogi~. 0. 1M~~PJt{~Ellic kson C7). 0. ( tt7\ ) MfB,giiJMMcF adden ( 22) '~11 0Jl. ( itt ) il1I!r,,~BtH'tm Debre (4 ) mmll:!. 0. a. ~1i~-OO A~~.z=1'~jlJ~~lJi§~1> tl'iI ~ ••.

(20) ~. 15 ~i1iiPJ~;ttx~~~'ilJ!MJmlJi EI Jl,lH13IWl (t.H ~.M:. ( [1:/\ ). 0. ~L o gitUI'f.J.:t~*l'itE~~~F<iI-OO,!lpg ( nested ) rnM~ZI1~{Jj~ffmFclttDtt tltZ. mJI'f.J1-t§,~.fl IJ::J"Fcl. 0. ( tt1L J !JI!l~:i:I!1-tPl'f.Jr.t~'ilJ~ ~McFad den ( 24) ~J]Ha u sman. *ff!'{j):) ~. '~2433i24 6Jf .. a nd McFadden ( 11 ) ~H2 28l'i. ( ~-j-- ) CJ$~ Amemiya. ' m=1'Fcl. ( 1 ) .miSmall and. McFa dden ( 25 ). ~ 1 4 25:ID.4 26 :g. •. 0. Brownstone (. 33 ) HI±I~-~fi5~1Ji'! • m~tt1 t. ( li nearized maximu m li kel ihood m ethod ) , ft!!1'P~~~ Jtti'! J:t :1C ?1 UUaffi:tEfi5wt. fl'E&l$i ( efficiency ) J:~ fIl'. IJl.tr.J:1'I1J1Jjj!fF~ "f?:mi1t~{JJ"M~~*. *t~Hj;(i:ll-:mIi'1l'. an. ilUCJIiE~H:t£ o. J • ~242 Ji ' !i39 *XiBfil3fT:al§-OO1E?tm.[\~*W1 {j;(ffirn ' m.-~.i'!~!n~IJ 1&iklY-J~~ • ,Rt!#f'Fli\. ( i t i - ) CJ$i!McFad den (24 ~i.t*f'5it. ~. 0. 0. ( ~+= )~~AGEIY-J~~~£-~~0~~ o-~jffig·~~~~~'I~~'~.~~'~~~. ~~I'f.J'~ ~_~*~~fIlM~z~~~J:t~~ o @tE~~~ffll'f.J~~~~~~(t-J ~~, ft!liflljflO~~. , ffi1'l1l.Rj>~~i!II?1iJ3~£ • ;lj5{!BIliJl6nNl~!&~1l!:. 0. ( ~+= ) YX!WIlYz..~ 'IltJJHEl¥:Ij>;'fWZf*Jf('ilJJ;..{)Vm1ilt~FclPJTi!J.*Rlf;t~~I¥.~WPJrD. ( +~. 3tf1I'f.J{ftlt. illingn ess-to - pay).. J m n~f;f.fi£- @.~Ii« ( fij--pmli ) ffil~ , ftiFII R.lH~rj- FA ~ (. ElijiJiE{:E1Utpgz~~. ifii wt~~M!RZJ1lIIl. ( inclu ive va luo) II4f ' iI&:, ~ :mjtj1t Il!};iE~ (other. ft(l;tt /iBCJI*!t'A*~. , PJrjl~~ ~~. ~'h~ffiIIHUIJIY-Jf!ti: 'J2..(~~. 0. EE 1itJlt1!Unl~PJtm~~. Rz ~fl. ). 0. alternatives ) ,. , :ftf!!m;ttIthUZ!jS!$JJlfH~tf. 0. ( if+n ) Shelton ( 32 ) £(~Ii!iJR:t4f5"rl ' *6~UnJi.tE-Mjt!r13~{:l:~iFJ2..(..tfiIJ.l~U!l4I~Uj( ~~;fIJ ' llB fIO£l "F~ftIJJ@:~Jf.l m.M1i ~ Kent ( 15 ) ,Ioannides and I'fenderson ( 14 J IIIJW~{:E"l:;;1l m ' tt 0. ~~~'&~~~ M~~~ ~~~ o. [I ] Amemiya, T ., "On a Two-Step E ·ti ma tion of Multivariat l ogit Model," Journal ofEco nometrics, Vo!' ,N o . I ,Aug. 1978 pp. 13-2 1. [2) Boehm, T. P., "Tenu re Choice an d Expected Mo bility ," Journal of Urban Economics, Vol. l O, No . 3,Nov. 198 1, pp . 37S-389 . l3 ] Boren , P, User's Guide to H ASE Da ta, Vol. 2: The Su rvey Files . R-26 92/ 2-HUD, T he Rand Corporatio n. California, April. 1982 . f41 De breu , G_. " Review of R. D. Luce Individual Choice Behavi r ," American Economic Review, Vol. 50,1 960 , pp . 186-1 88 . [5] Deleeuw, F., "The Demand for Housing: A Review of the Cross Section Evidence ." Re view of Economics and Statistics, Vol. 53, No . 1. F eb. 1971 , pp. 1-10. [6 ] E llickson. B., " Local Public Goods and the Market for eighborhoocis," in D. Segal , cd ., The Economics ofNeighborhood, 1977 . pp. 263 -292. [7] Ellic kson , B., .. An Alte rnative Test of th e Hedo nic Theory of Hou sing Marke ts," Jour nal of Urban Economics, Vol. 9 No. I . Feb. 198 1. pp . 56 -79 ..

(21) - 157 [8] Ellickson , B.. " Indivisibility, Ho using Ma rke ts an d Pub lic Goods," in J. V . Henderson ed., Research in Urball Economics, Vol. 3 , 1983 , pp . 91 -166 . [9J Gillinghan , R ., and R. Hage mann . "Cross-Se ction al Es tim a tion of a Si.multaneous Mod el of Tenure Chu ice and Housing Se rvice Demand ," Journal of Urban Economics, Vol. 14 . No . 1. J uly 1983 . pp.16-39. /10) G ross, D. J .. "Estimating Willingness-to-Pay for Housing Ch aracteristics: An Application of the Ellickso n Bid- Re nt Model," Manuscripts, Public Administration Institute, Louisiana State University , Jan. 1986. [II) Hausman. J. A., and D. McF adden , "A Specification Test for the Mu ltino mial Logit Model. " Ecunometrica, Vol. 52. No .5 , Sep t. 1984, pp. 1219-1240. (1 21 He nders on, J. V. , and Y . M. Ioannides , "A Mo del of Hoosing Ten ure Choice," A merican Economic R el'iew, Vol. 73.19 83, pp . 98 -103. [13] H orowitz, J. L., " Bidding Mo dels of Hou sing Market." Journal of Urban Economics, Vol. 20 , No .2. Sept. 1986 . pp. 166-190. (14] l o annid es, Y. M., and J. V. He nde rson , "Te nure Choice of the De mand fur Housing: A Supplem ent ," Project of NBER, Fe b . J 984. [15] Ken t, R . 1.. " The Relationships between Income and Price Elasticities in Studies o f Ho using Demand , Tenure ChOice and Housi ng Fo rmation," Journal of Urban Economics, Vol. 13, No . I , J an. 1983. pp. 196-204. (16) King, M. A., " An Econometric Mod el of Tenure Choice and Dem and for Housing as a Joint Dec ision ," Journal ofPublic Economics, VoI.14 ,N o. 2,Oct. 1980,pp . 137- 159. [17] Lee, L. F. , and R . P. Trost. " Estimation of Some Limi te d Dependen t Variable Models with Ap plication to Housing Demand," Jou rnal of Econometrics, Vol. 8, No .3 , Dec . 197 8 , p p. 357-382. [18] Lee , T. 1-1 .. " Hou sing an d Permane nt Income: Tests Base d on a Three -Year Re interview Survey ," Review of Economics & S tatistiCS, Vo l. 50 , No .4, N ov. 196 8 , pp. 480-490. [19J Lerma n, S . R. , an d C. R. Kern , " Hedo nic T heory , Bid Re nts, and Wi llingness-to-Pay: Some Ex tensi o ns of Ellickso n 's R ~suJt s , " Journal of Urb an Economics, Vol. 13. No.3 , May 1983 , pp. 3 58-363. [20) Li , M. M. (1977), "A Lo git Mode l of Ho meo wnership, " Econometrica, Vo. 45 , No .5, July 1977, pp. 1081-109 1. 121] Lowry . I. S .• Experimenting wilh Housing Al lowance . Oelgesch lagers. G unll & Hain, Publishers, Inc ., Ca mbridge. Ma ., 1984. [22] Mc F adden, D .• " Co nditional LOgit Analysis of Qu alitative Ch oice Be havi o r," in P. Za rcmb ka ed ., Frontiers in Econometrics, p p. 105- 142 , 1974. (23) Mc Fad de n, D ., " Modelling th e Choice of Reside ntial Location ," in A . Ka rlquist et al ed., Spatial Interaction T71eolY and Resid en tial Location, pp. 75-96 , 1978 . [24) McFad den , D. , "Eco nome tri c Models u fP robabilities an d Structural Anal ysis of Discrete Da ta with Choice ," in C. Ma nski and D. McF add en " eds .. Econometric Applications, 198-1272, J981. (25 ) Mc Fadden, D ., "Econome tric Analysis of Qualitative Res po nse Model ," in Z. Griliches and M. D. lntriligator eds., Handbook of Econome tn'cs, Vol. 2, pp. 1395 -1457, 1984..

(22) - 158 [26 ] MUlh, R . F., " The Deman d for Non-Fa rm Housing," in A. . Harberger, ed., The De mand fo r Durable Goods , pp . 29-96 , 1960. l27 ] Polinsky, A. M., ' The De mand fo r Housing: A Study in Specificati on and Grol;pi ng." Econometrica, Vol. 45, No . 2, Ma rch 2979, pp. 447-461 . (28) Reid M., Ho using and Income, UniversiLY 0 Chicago Press, 1962 .. 1291 Ro en, H. S.. and K. T. Rose n, Fe dera l Taxes an d Ho me ownership Evid nee from Time [30J (3 1J [32]. l33]. 1'341 [35 ) [ 6]. Series," Journal o/ Po litical Economy, Vol. 8 , o. I, Fe b 1980, pp. 59-75 Rose n, H. S., K. T. Rose n and D. Holtz-Eakin, 'Hou ing Te nure , Uncertainty nd Taxa ti on," Review of Econom ics & Statistics, V L 66 , No_ ,Aug. 1984, pp_405-4 16. Rose n, S., " Hedonil: Price, and Implicit Markels: Produ t Diff rentiatio n in Pure Camp titioll ," JO Lirnal ofPolitical Ecunomy, Vol. 82, o. I , lan./Fe b. I 74, pp . 34-5 - . he lt on . J. P. , 'The Cost of Re nting Versus Owning a Home ," Land Economics, Vol. 44 . No. I Feb . I 96l:l, pp. 5 -7l. Small , K., and D. Brownstone . "Effj~ie nt Estimatio n of Nested Logit Mod Is," Manu script. School ofSo cio.l Sci(!nce, University f ali fo rn ia at Irving. J une, 1989. Swa n, C_, "A Mo cie l of Ren tal and Ow ner-Occupie d Hou ing: Journal of Urban Econo m ics, Vo l. 1 . o. J , N v. 1984, pp. 297-316 . Varian, H. R .. Microec unomic naly ~ i . Univer i y o f Mil:higan Press, 1978 . Winger. A. R., "Housi ng and In L:ome ," Western E. I ., 6 , 1 6 8, pp. 226-2 2.. ABSTRACT A household could potentially be a renter or an owner. Deciding his housing consump tion, a household usually determines his tenure choic , too. Literature on rhe topic ofhOLl ing demand have never specifically estimated the substitution o f rental housing and owner-occu pied housing. This paper discusses a household utility maximizing behavior incorporating housing demand and tenure choice, then builds a 'reversed nested multinom ial logit mode/'. Applying the data from the Ho using Assistance Supply Experiment in the U. S., we estimate the substitution o f rental housing and owner-occupied housing, and find that they do have a high substitutibility, though it varies between cities by their housing market conditions..

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