1# RECORD 1b4b730b1b7e5c68c755a7ae46919b1120fc8c062# TITLE: Housing Markets in Uruguay: Determinants of Housing Demand and Its Interaction with Public Policies3# YEAR: 2011.0 VENUE: nan DOI: 10.18235/00113484# AUTHORS: Natalia Garabato; Magdalena Ramada-Sarasola5# PAGES: 70 CANDIDATE TABLE PAGES (1-based): [15, 21, 24, 25, 27, 28, 33, 34, 36, 37, 38, 40, 58, 64, 65, 66, 67, 68, 69, 70]6# ABSTRACT: This paper analyzes the determinants of housing demand for Uruguay and the extent to which housing policies have an impact on their target population. The paper first analyzes the determinants of housing demand, following an approach based on Rosen's (1974) two-step procedure consisting of fitting a hedonic price regression in 34 different geographical units (or markets) to estimate a housing demand function. The determinants of formality and ownership choices were examined using a multinomial logit framework. Determinants of these choices include both household demographic attributes and access to and use of public housing programs and other social programs. Policy recommendations are offered on the basis of the finding that a price and income-inelastic formal housing market greatly contrasts with a rather price and income-elastic informal housing market.789===== PAGE 1 (score 0, numeric-paren 0, table-words 0, var-words 0) =====1011Housing Markets in 12Uruguay: 13Determinants of Housing Demand 14and its Interaction with Public Policies15Natalia Garabato 16Magdalena Ramada-Sarasola17Department of Research and Chief Economist18IDB-WP-25819IDB WORKING PAPER SERIES No. 20Inter-American Development Bank21October 2011222324===== PAGE 15 (score 31, numeric-paren 18, table-words 3, var-words 1) =====252612 27 28Table 1. Determinants of Income: Box-Cox Regression 29 30 Coef. 31 P>32)33(342 df35χ36 37 Elasticity 38Transformed 39Age 40 279.851*** 41 0.000 42 0.228 43Not Transformed 44Male 45 0.001 46 0.002 47 0.001 48Caucasian 49 0.127*** 50 26.103 51 0.078 52Primary 53 0.495*** 54 459.675 55 0.304 56Secondary 57 1.084*** 58 1197.778 59 0.667 60Technical 61 1.303*** 62 925.828 63 0.801 64Tertiary 65 2.162*** 66 1750.546 67 1.329 68Postgraduate 69 2.939*** 70 546.598 71 1.806 72Inc. Retiree 73 -0.688*** 74 305.375 75 -0.423 76Inc. Informal 77 -1.304*** 78 1109.138 79 -0.802 80Inc. Private 81 -0.329*** 82 72.744 83 -0.202 84Inc. Public 85 -0.257*** 86 38.080 87 -0.158 88Inc. Other 89 -0.996*** 90 112.402 91 -0.612 92MVD 93 0.391*** 94 483.331 95 0.241 96With Partner 97 0.815*** 98 848.897 99 0.501 100Divorced/Widower 101 0.196*** 102 44.223 103 0.120 104Constant 105 -157.161 106 107 108 109 Coef. 110 P>|z| 111 112λ 113 -1.66*** 114 0.000 115 116θ 117 0.053*** 118 0.000 119 120N 121 17,030 122 123 124LR 1252126χ 127 8,259.95 128 129P> 1302131χ 132 0.000 133 134 135Test HO: 136 137 138 139 140 141 P> 1422143χ 144 145θ =λ =-1 146 147 0.000 148 149θ =λ =0 150 151 0.000 152 153θ =λ =1 154 155 0.000 156 157 *p<0.1, **p<0.05, ***p<0.001 158 159As can be seen in Table 1, likelihood-ratio tests reject the linear, log-linear and inverse-160multiplicative specifications at the 1 percent significance level, being θ and λ 0.053 and -1.66, 161respectively, and hence supporting the choice of a Box-Cox specification. Given the obtained 162coefficient for λ income relates to age in an inverse U-shape, as expected. Transformed age has 163a positive elasticity of 0.228, a result that is slightly lower than the one obtained by Fontenla et 164al. (2009) for Mexico. Further, we obtain a positive effect on the household’s total disposable 165166167===== PAGE 16 (score 4, numeric-paren 0, table-words 0, var-words 4) =====16816913 170 171income when the head of household head is male and Caucasian. The percentage changes on a 172household’s total income for a male head of household (as opposed to a female one) and from 173having a Caucasian head of household (as opposed to another race) are 0.1 percent and 7.8 174percent, respectively. The effect stemming from gender is so little since gender refers to the head 175of household, while income includes the whole household’s income, i.e. the income received by 176each household member, plus the entire household’s income from other sources such as 177remittances. 178Further, we obtain increasing elasticities for each consecutive completed education level, 179with an impact of almost tripling income for finished postgraduates. The impact of completing 180primary education is 30.4 percent, and a secondary degree generates an income premium of 66.7 181percent with respect to households in which heads have not completed primary education. 182The results for variables capturing the type of source of income are also aligned with 183economic intuition. An informal source of income has a negative impact of income when 184compared to owning a business (omitted case), while public and private salaried are those 185displaying the smallest difference in permanent income when compared to business owners. 186Owning a business as the main source of income earns a premium of over 80 percent over having 187an informal source of income, while households headed by retirees and pensioners have 42 188percent less total disposable income. 189As expected, cities in the upcountry display smaller permanent income levels than 190Montevideo. The income premium of living in the capital is 24 percent. Finally, biparental 191households have an income premium of almost 50.1 percent, while monoparental households 192with a divorced or widowed household head earn 12 percent more income than those led by 193singles not engaged in a stable life-partnership. 194The highly significant results obtained, as well as their correctly predicted direction, 195support the approach of estimating permanent income through the above regression, while 196temporary income is defined as the residual. The estimated permanent income, noted as y , is 197used in the next estimation stages. 198 1993.2 Estimation of Hedonic Prices 200 201For all 34 geographic units defined above, we estimate hedonic price regressions testing among 202different specifications of the Box-Cox transformation. In all regressions the household head’s 203204205===== PAGE 21 (score 42, numeric-paren 27, table-words 1, var-words 3) =====20620718 208 209between the formal and the informal market can be explained by the fact that a higher price 210elasticity is to be expected for low-income households, and it also mirrors the higher degree of 211mobility in informal markets. In these markets, supply seems to match better the quantity of 212demand since it is more flexible as it is not regulated. Given Malpezzi (1999) and Zabel’s (2004) 213results on the high degree of variation of price elasticities and its sensitivity to model design, our 214results are in line with expectations, and it can be argued that the informal market behaves as a 215more homogeneous market in the sense that households in informal markets are more prone to 216relocate to other informal housing arrangements irrespective of neighborhood or area. In the 217formal market, where there is less supply than demand and where less housing substitutes are 218available, demand becomes price inelastic. 219 220Table 3. Housing Demand: Box-Cox Regression 221 222Total sample 223Formal 224Informal 225 226Coef. 227Elasticity 228Coef. 229Elasticity 230Coef. 231Elasticity 232Not Transformed 233Temporary Income 2340.000*** 2350.032 2360.000*** 2370.038 2380.000*** 239-0.023 240Male 241-0.209*** 242-0.087 243-0.155*** 244-0.063 245-0.364** 246-0.132 247With Partner 2480.019 2490.008 2500.011 2510.004 252-0.102 253-0.037 254Children<14 255-0.046*** 256-0.013 257-0.006 258-0.001 259-0.119*** 260-0.060 261# adults in HH 2620.007 2630.007 2640.014 2650.013 2660.062 2670.057 268Education HH head 2690.125*** 2700.113 2710.137*** 2720.123 273-0.028 274-0.018 275Caucasian 2760.255*** 2770.106 2780.243*** 2790.098 2800.067 2810.024 282Use Housing Policies 283-0.011 284-0.004 285-0.117*** 286-0.047 287- 288- 289Financial Access 2900.243*** 2910.101 2920.179*** 2930.072 294-0.283 295-0.102 296Social assistance 297-0.355*** 298-0.147 299-0.346*** 300-0.140 301-0.109 302-0.039 303Const. 3043.472 305 3063.92 307 3087.299 309 310 311Transformed 312Price Index 313-0.368*** 314-0.372 315-0.368*** 316-0.368 317-0.728*** 318-0.792 319Permanent Income 3200.106*** 3210.373 3220.091*** 3230.325 3240.105*** 3250.570 326Age 3270.194*** 3280.200 3290.2*** 3300.205 331-0.12** 332-0.134 333 334Coef. 335P>|z| 336Coef. 337P>|z| 338Coef. 339P>|z| 340λ 3410.228 3420.000 3430.231 3440.000 3450.297 3460.000 347σ 3481.183 3490.000 3501.178 3510.000 3521.417 3530.000 354N 35516,939 356 35713139 358 3591054 360 361LR 3622363χ 3645375.7 365 3663661.77 367 368359.33 369 370P>3712372χ 3730.000 374 3750.000 376 3770.000 378 379Test HO: 380 381P>3822383χ 384 385P>3862387χ 388 389P>3902391χ 392λ =1 393 3940.000 395 3960.000 397 3980.000 399λ =0 400 4010.000 402 4030.000 404 4050.000 406λ =1 407 4080.000 409 4100.000 411 4120.000 413 *p<0.1, **p<0.05, ***p<0.001 414415416===== PAGE 22 (score 5, numeric-paren 0, table-words 0, var-words 5) =====41741819 419 420For the overall market, income elasticities obtained for temporary and permanent income 421are in line with those found in the literature. In fact, while temporary income is found to have an 422elasticity of 0.032, permanent income shows an elasticity of 0.373, which, although being 423smaller than the usually expected 0.6 to 0.8 income elasticities, is at least above the minimum 424income elasticities reported in literature for developed countries, which are around 0.36 (Mayo, 4251981). Here again, the informal market yields an income elasticity that is higher and closer to 426what is expected—0.57—pointing again to the fact that the informal market seems to be working 427as a more consolidated housing market. These results seem to be robust to changes in the 428definition of neighborhoods and proper housing markets6429In addition, our results seems to be consistent with the findings from Garabato and 430Ramada-Sarasola (2011) on the types of qualitative deficits faced by households in Uruguay and 431the lack of use of housing programs by the eligible population. In this sense, Uruguayan 432households seem to display a certain inertia with respect to improving their housing conditions, 433and this may be also reflected in the low elasticity found with respect to income. Given that the 434regression displays a static relationship between income and housing, this may be also the result 435of neglecting a time lag or taking into account income evolution, rather than income levels. 436, as well as to different ways of 437segmenting housing demand according to income strata or geographic areas. 438When looking at household composition in terms of its members, we find that the larger 439the number of children, the less housing is demanded—and less income is available to demand 440housing given the costs children entail. This result seems to be highly driven by the informal 441market, where the elasticity is more than double the one found in the whole sample. In the formal 442market, no significant effect is found. In addition, the usual negative impact on housing demand 443of being married, reported in the literature, is being captured by the presence of the variable 444accounting for the number of children. Once that has variable been controlled for, as well as total 445income (which implicitly includes the number of earning adults in the household), neither the 446partnership variable nor the number of adults in the household yields significant results. 447In the overall sample, male heads of household demand 8.7 percent less housing than 448their female peers, Caucasian heads of household demand 13 percent more housing, and bi-449parental households demand 10.6 percent more housing than their single counterparts. Further, 450we find that age has a positive impact on housing demand, with an elasticity of almost 0.20, both 451 4526 Results are available upon request. 453454455===== PAGE 24 (score 28, numeric-paren 18, table-words 1, var-words 2) =====45645721 458 459housing demand. None of these effects can be observed among informal housing markets. The 460fact that the use of housing programs has a significantly negative impact on the amount of 461housing demanded, even after controlling for the effect stemming from the household's 462socioeconomic attributes, may imply that housing programs grant access to housing solutions 463with fewer attributes than those that would have been demanded if the housing unit had been 464acquired by other means, all other things being equal. 465 4663.3.2 Housing Demand in Montevideo 467Given the low price elasticities found for housing demand when taking into account all 468Uruguayan cities with more than 5,000 inhabitants, the question arises whether the result is 469driven by low mobility from one urban area to the other across the country. We looked at the 470results of a much more homogeneous market, in which analogous housing units should behave as 471substitutes irrespective of them being in different neighborhoods. 472 473Table 4. Housing Demand: Box-Cox Regression for Montevideo 474 475Montevideo 476Montevideo Formal 477Montevideo Informal 478 479Coef. 480Elasticity 481Coef. 482Elasticity 483Coef. 484Elasticity 485Not Transformed 486Temporary Income 4870.000*** 4880.030 4890.000*** 4900.038 4910.000*** 492-0.033 493Male 494-0.137** 495-0.037 496-0.038 497-0.011 498-0.31* 499-0.094 500With Partner 501-0.035 502-0.010 5030.006 5040.002 505-0.043 506-0.013 507Children<14 508-0.049** 509-0.008 5100.031 5110.004 512-0.129** 513-0.053 514# people in 515household 5160.057** 5170.036 5180.075*** 5190.053 5200.092 5210.073 522Education HH head 5230.092** 5240.059 5250.124** 5260.092 527-0.011 528-0.006 529Race White 5300.328*** 5310.089 5320.330*** 5330.099 534-0.073 535-0.022 536Use Housing 537Policies 538-0.034 539-0.009 540-0.189*** 541-0.057 542 543 544Financial Access 5450.340*** 5460.092 5470.187** 5480.056 549-0.41 550-0.125 551Social assistance 552-0.432*** 553-0.117 554-0.359*** 555-0.108 5560.034 5570.010 558Const. 5595.078 560 5615.421 562 5639.814 564 565566567===== PAGE 25 (score 17, numeric-paren 10, table-words 1, var-words 1) =====56856922 570 571Table 4., continued 572 573 574Montevideo 575Montevideo Formal 576Montevideo Informal 577 578Coef. 579Elasticity 580Coef. 581Elasticity 582Coef. 583Elasticity 584Transformed 585Price Index 586-0.291*** 587-0.319 588-0.322*** 589-0.350 590-0.843*** 591-0.897 592Permanent Income 5930.047*** 5940.321 5950.042*** 5960.239 5970.072*** 5980.498 599Age 6000.200*** 6010.208 6020.217*** 6030.223 604-0.139** 605-0.153 606 607Coef. 608P>|z| 609Coef. 610P>|z| 611Coef. 612P>|z| 613λ 6140.337 6150.000 6160.305 6170.000 6180.340 6190.000 620σ 6211.638 622 6231.399 624 6251.642 626 627N 6287,129 629 6305,410 631 632775 633 634LR 6352636χ 6371,980.21 638 6391,423.59 640 641251.05 642 643P>6442645χ 6460.000 647 6480.000 649 6500.000 651 652Test HO: 653 654P>6552656χ 657 658P>6592660χ 661 662P>6632664χ 665λ =1 666 6670.000 668 6690.000 670 6710.000 672λ =0 673 6740.000 675 6760.000 677 6780.000 679λ =-1 680 6810.000 682 6830.000 684 6850.000 686 *p<0.1, **p<0.05, ***p<0.001 687 688 689Although the elasticities for the overall market and for the formal market seem to 690decrease, i.e., become more inelastic, the difference between them is not significant. In the case 691of the informal market, where mobility across the country is even more difficult than for the 692formal market, the change goes in the expected direction, with elasticities becoming significantly 693larger in absolute terms. In other words, within the informal market results taking into account 694the whole country were downward, biasing price elasticity. For the formal Montevideo housing 695market, no difference can be found and price elasticity remains low. Still another difference is 696the effect of male heads of household demanding less housing, which becomes less pronounced 697in Montevideo. 698 6993.3.3 Housing Demand by Owners and Tenants 700Arguably, income elasticity should be greater for tenants, who can adapt their housing solutions 701to changes in income faster and face fewer transaction costs than homeowners. In fact, in 702Uruguayan homeowners incur transaction costs both for selling and for buying a new housing 703unit, among them income taxes, notary costs, and real estate brokerage fees. Coordinating the 704timing of selling and buying simultaneously to transfer the value of one housing unit to the next 705706707===== PAGE 26 (score 11, numeric-paren 0, table-words 2, var-words 7) =====70870923 710 711also should increase the difficulty faced by owners in adapting their housing solution to both 712their needs and changes in income. On the other hand, owners could refurbish and renovate their 713housing units as a reaction to an increase in income. This could offset the effect of transaction 714costs for improving their housing conditions in the case of owners, provided they have access to 715credit. If there is no working lending market, it may be difficult for owners to borrow a large 716amount of money to adapt their housing unit, despite their current income having increased. This 717reasoning warrants estimating the demand separately for owners and tenants to see whether and 718how their price and income elasticities differ. 719As can be seen in Table 5, the housing demand of tenants is more elastic to changes in 720housing price, which supports the inference that tenants have more ability to react to changes in 721prices given they are the only ones who actually act as real economic agents in the market. While 722for owners there is a so-called imputed rent, if prices go up, the odds of an owner moving due to 723the movement in prices are rather small. 724In addition, our results support the inference that tenants are more income elastic than 725homeowners. Both elasticities are around 0.3, i.e., aligned with the previous results presented 726above. The difference, although not large, is significant. Given that the elasticities are similarly, 727one could also conclude that for owners, the lack of a well-functioning mortgage market may be 728keeping the income elasticity low. For tenants—where this situation does not apply—restrictions 729on the supply side, inertia, and other types of transaction costs, such as finding proper rental 730guarantees, may explain the low elasticity. 731Further differences become apparent between owners and tenants. First, while for owners 732neither the fact of having a partner, nor the number of grown-ups in the households affects 733housing demand, it does so for tenants. According to our results, couples that rent demand less 734housing, other things being equal, than single people. Given that the variable on the number of 735children is non-significant,8736 7378 This result is probably related to the high correlation between the two variables and a smaller number of 738observations in the subsample for tenants. 739 this may be capturing the effect of couples demanding less housing 740given that they have to spend on other items, such as schooling and health care for their children. 741The interesting fact is that this is not observable for owners, who do not appear to reduce their 742demand for housing in order to account for family-related expenses when the housing demand is 743felt to be related to a more permanent type of housing, i.e., when buying. Older heads of 744745746===== PAGE 27 (score 31, numeric-paren 21, table-words 1, var-words 1) =====74774824 749 750household also tend to demand more housing when buying than when renting, displaying an 751elasticity to age that almost doubles the one for tenants. 752 753Table 5. Housing Demand: Box-Cox Regression for Homeowners and Tenants 754 755Homeowners 756Tenants 757 758Coef. 759Elasticity 760Coef. 761Elasticity 762Not Transformed 763Temporary Income 7640.000*** 7650.037 7660.000*** 7670.038 768Male 769-0.164*** 770-0.064 771-0.076 772-0.034 773With Partner 7740.02 7750.008 776-0.136** 777-0.061 778Children<14 779-0.009 780-0.002 781-0.004 782-0.001 783# people in household 784-0.014 785-0.013 7860.106*** 7870.101 788Education HH head 7890.143*** 7900.121 7910.108** 7920.115 793Race White 7940.249*** 7950.097 7960.197*** 7970.088 798Use Housing Policies 799-0.209*** 800-0.081 8010.212*** 8020.095 803Financial Access 8040.097** 8050.038 806 807 808Social assistance 809-0.381*** 810-0.148 811-0.276*** 812-0.123 813Const. 8144.434 815 8164.103 817 818Transformed 819Price Index 820-0.351*** 821-0.346 822-0.384*** 823-0.410 824Permanent Income 8250.087*** 8260.318 8270.101*** 8280.345 829Age 8300.151*** 8310.154 8320.077** 8330.080 834 835Coef. 836P>|z| 837Coef. 838P>|z| 839λ 8400.238 8410.000 8420.216 8430.000 844σ 8451.240 846 8470.93835 848 849N 85010,714 851 8522,425 853 854LR 8552856χ 8572714.87 858 859895.05 860 861P>8622863χ 8640.000 865 8660.000 867 868Test HO: 869 870P>8712872χ 873 874P>8752876χ 877λ =1 878 8790.000 880 8810.000 882λ =0 883 8840.000 885 8860.000 887λ =-1 888 8890.000 890 8910.000 892*p<0.1, **p<0.05, ***p<0.001 893Note: Only the formal housing market was considered for the regression above. 894 895Finally, while, ceteris paribus, access to housing through housing programs reduces the 896amount of housing demanded for households becoming owners of a housing unit, the result is 897also significant, in the other direction, for tenants. In other words, tenants that make use of the 898rental guarantee fund are able to demand more housing. 899 900901902===== PAGE 28 (score 54, numeric-paren 37, table-words 1, var-words 1) =====90390425 905 9063.3.4 Housing Demand by Age Cohorts 907Segmented housing demand can further analyzed. Given the high degree of inertia present in 908Uruguayan households, which appears to be preventing them from adjusting their housing needs 909to changes in income, coupled with the aging of the Uruguayan population, it seems natural to 910ask if inertia may be related to heads of household overconsuming housing at older ages and not 911changing their housing solution after a certain age—say, 50—when they reach the maximum 912amount of housing they can demand. 913 914Table 6. Housing Demand: Box-Cox Regression by Age Cohorts 915 916 917Age <=30 918Age 31-50 919Age 51-65 920Age 66+ 921 922Coef. 923Elasticity 924Coef. 925Elasticity 926Coef. 927Elasticity 928Coef. 929Elasticity 930Not Transformed 931Temporary Income 9320.000*** 9330.029 9340.000*** 9350.038 9360*** 9370.045 9380*** 9390.033 940Male 941-0.144** 942-0.094 943-0.041 944-0.026 945-0.325*** 946-0.099 947-0.225** 948-0.074 949With Partner 950-0.179** 951-0.117 952-0.038 953-0.024 9540.113 9550.034 9560.103 9570.034 958Children<14 959-0.021 960-0.013 9610.002 9620.001 963-0.121** 964-0.012 9650.012 9660.000 967# people in household 0.182*** 9680.228 9690.017 9700.028 9710.01 9720.008 973-0.006 974-0.004 975Education HH head 9760.077* 9770.128 9780.066** 9790.107 9800.204*** 9810.140 9820.188** 9830.112 984Race White 9850.099 9860.065 9870.163*** 9880.104 9890.297*** 9900.091 9910.312*** 9920.103 993Use Housing Policies 9940.07 9950.046 996-0.106*** 997-0.068 998-0.18** 999-0.055 1000-0.113* 1001-0.037 1002Financial Access 10030.288** 10040.188 10050.139** 10060.089 10070.156 10080.048 10090.162* 10100.053 1011Social assistance 1012-0.132** 1013-0.086 1014-0.213*** 1015-0.136 1016-0.421*** 1017-0.128 1018-0.74*** 1019-0.243 1020Const. 10212.553 1022 10232.846 1024 10255.429 1026 10274.863 1028 1029Transformed 1030Price Index 1031-0.32*** 1032-0.333 1033-0.369*** 1034-0.370 1035-0.397*** 1036-0.390 1037-0.364*** 1038-0.363 1039Permanent Income 10400.112*** 10410.215 10420.183*** 10430.349 10440.063*** 10450.325 10460.068*** 10470.298 1048Age 10490.339** 10500.325 10510.111* 10520.109 10530.155 10540.160 10550.17** 10560.189 1057 1058Coef. 1059P>|z| 1060Coef. 1061P>|z| 1062Coef. 1063P>|z| 1064Coef. 1065P>|z| 1066λ 10670.118 10680.000 10690.114 10700.000 10710.299 10720.000 10730.281 10740.000 1075σ 10760.662 1077 10780.725 1079 10801.582 1081 10821.437 1083 1084N 1085873 1086 10874,306 1088 10893,829 1090 10914,131 1092 1093LR 109421095χ 1096194.660 1097 10981113.880 1099 11001113.880 1101 1102885.280 1103 1104P>110521106χ 11070.000 1108 11090.000 1110 11110.000 1112 11130.000 1114 1115Test HO: 1116 1117P>111821119χ 1120 1121P>112221123χ 1124 1125P>112621127χ 1128 1129P>113021131χ 1132λ =1 1133 11340.000 1135 11360.000 1137 11380.000 1139 11400.000 1141λ =0 1142 11430.004 1144 11450.000 1146 11470.000 1148 11490.000 1150λ =-1 1151 11520.000 1153 11540.000 1155 11560.000 1157 11580.000 1159*p<0.1, **p<0.05, ***p<0.001 1160 1161 1162We estimated the housing demand regression for four age cohorts that should proxy four 1163different stages of a household. In the first stage—heads under 30 years of age—households are 1164new and have to face several sources of costs, e.g., access to durable goods, which may reduce 116511661167===== PAGE 29 (score 2, numeric-paren 0, table-words 0, var-words 2) =====1168116926 1170 1171their income elasticity. In the second stage—heads of household between 30 and 50 years of 1172age—households are stabilizing their housing situation and accessing something permanent. It is 1173also the stage in which income varies the most and where expenses related to children are the 1174highest. In the third stage, households should have a smoother income pattern, and while income 1175varies less, housing needs should decrease since in this stage children leave home. Finally, in the 1176fourth stage—heads of household 65 or older—income may be reduced due to retirement. This 1177should be coupled with a reduction in housing demand. 1178The regression results support most of these inferences. According to our results, the 1179function of income elasticity depending on age displays a clear inverse U pattern, with a 1180maximum elasticity found in stage 2, the stage in which income has more variation, decreasing 1181both for the earlier and the later stages. The results also point towards higher elasticities for 1182households in the fourth age cohort than for those in the first one. This does not support the idea 1183of households overconsuming housing in older ages. In a country like Uruguay, this is an 1184important policy issue. According to our results, households are less income elastic in their 1185constitution stage. This is because in this stage, income is also used to access other goods—1186automobiles, durable goods—and housing may not be the first priority to adapt, once a minimum 1187amount of housing has been provided for. In the next stage, income elasticity increases by 60 1188percent, coupled with the fact that income increases during this stage, given that this is the most 1189productive age cohort. Housing needs also increase, as this is the stage in which households 1190grow the most. After that, income elasticity decreases, although not as much as expected. This 1191may imply that households continue to reduce their housing consumption in older ages, although 1192clearly not as much as needed, since elasticity decreases for the last two cohorts, where housing 1193needs also decline and income is rather stable. 1194Finally, it also appears that access to financing is most important in terms of increasing 1195housing demand for younger households. The importance of access to financing decreases as 1196households age. In addition, while access through housing programs is not significant for the 1197youngest households, it also has elasticities which in absolute terms decrease with age. This is 1198interesting since it implies that housing programs seem to improve for older age cohorts, in terms 1199of restricting less the housing demand of households, other things being equal. 1200 120112021203===== PAGE 33 (score 32, numeric-paren 25, table-words 2, var-words 1) =====1204120530 1206 1207decided to enter the informal market because they had no economic means to stay in the formal 1208market, while another 50 percent says that the trigger was the need to leave their parents’ 1209household (Amarante and Caffera, 2003). In that sense, it could be argued that entering 1210informality can be modeled as a decision in which demographic attributes play an important role. 1211Most of the variables display the expected sign and are highly significant in explaining 1212formality choices. In particular, the older and more educated the head of household, the more 1213likely he/she is to be in the formal market. An increment of ten years in age increases the 1214probability of being in the formal market by 0.01, and escalating one educational category (for 1215example from primary to secondary) increases the likelihood of choosing the formal market by 12160.023. The coefficient for the male dummy shows that women are 0.014 more likely to be a part 1217of the formal market. Finally, although years of tenure significantly increases the odds of 1218formality, the size of that effect is not great. 1219 1220Table 7. Formality Choices: Logit Estimation 1221 1222ME for Formal Market/ (se) 1223Age 12240.001*** 1225 1226(0.000) 1227Male 1228-0.014*** 1229 1230(0.003) 1231Caucasian (d) 12320.021*** 1233 1234(0.004) 1235Education level 12360.023*** 1237 1238(0.002) 1239Inc. Retiree 1240-0.032** 1241 1242(0.012) 1243Inc. Informal 1244-0.123*** 1245 1246(0.029) 1247Inc. Private 1248-0.053** 1249 1250(0.018) 1251Inc. Public 1252-0.045** 1253 1254(0.020) 1255Inc. Other 12560.001 1257 1258(0.022) 1259MVD 1260-0.063*** 1261 1262(0.004) 1263With Partner (d) 12640.006** 1265 1266(0.003) 1267# children under 14 1268-0.006*** 1269 1270(0.001) 1271Fin. Access 12720.026*** 1273 1274(0.003) 127512761277===== PAGE 34 (score 13, numeric-paren 6, table-words 1, var-words 4) =====1278127931 1280 1281Table 7., continued 1282 1283 1284ME for Formal Market/ (se) 1285 1286 1287Transfers 12880.005** 1289 1290(0.003) 1291Social assistance (d) 1292-0.022*** 1293 1294(0.004) 1295Years of Tenure 12960.000*** 1297 1298(0.000) 1299Pred. Prob. at mean 1300.971 1301Log Likelihood 1302-2753.259 1303Pseudo 130421305R 13060.2667 1307N 130814,193 1309*p<0.1, **p<0.05, ***p<0.001 1310ME: Marginal Effects. Marginal effects are calculated for the formal 1311market 1312 1313With respect to sources of income, all categories decrease the odds of being in the formal 1314housing market, when compared to owning a business. As expected, those that are more prone to 1315be in the informal housing market are the ones with informal sources of income. In fact, having 1316an informal source of income increases the odds of informality by 0.123. Surprisingly, private 1317and public salaried workers are more prone to be in the informal market than retirees and 1318pensioners, despite the latter being a group with a smaller average income. This can be reflecting 1319the fact that older people are less prone to enter an irregular housing market. Indeed, most 1320households in irregular settlements are headed by very young people, who see their irregular 1321situation as transitory. Still, as Garabato and Ramada-Sarasola (2011) have shown, for half the 1322population in irregular housing settlements, informality is not transitory. 1323Household composition also has an influence over formality decisions. Every additional 1324child under the age of 14 increases the probability of choosing the informal market by 0.006, and 1325monoparental households are more likely to be found in informal markets. Finally, those 1326households receiving social assistance are 0.022 more likely to be part of informal housing 1327markets, which is expected, as these households are probably in a vulnerable financial situation 1328that hampers their ability to access the formal market. 1329Households receiving income through transfers or remittances are 0.005 more likely to be 1330in the formal market than those who do not, and the impact of having access to external 1331financing sources is significantly positive, increasing the likelihood of formality by 0.026. This 133213331334===== PAGE 35 (score 6, numeric-paren 0, table-words 2, var-words 2) =====1335133632 1337 1338again supports the inference that there is a formality premium that households may not be willing 1339or able to pay. 1340 13415.2 The Ownership Choice 1342 1343Table 8 shows the marginal effects estimates and standard errors for the two ownership models 1344presented in the previous section. In the case of the formal market, households only have a 1345dichotomous choice to make (to rent or to own) and hence the fitted model is simply a binomial 1346logit regression in which the decision to own is taken as the base case. In the case of the informal 1347market, households can choose to own, rent, or occupy, and therefore a multinomial logit model 1348could be fitted. Still, given the scant number of households renting within the informal market, 1349we decided to pool renting and occupying together in a category that accounts for a less 1350permanent status within the informal market, against ownership, which is inherently a more 1351permanent situation of housing informality. The marginal effects of choosing between a 1352somewhat permanent and a less permanent housing solution within the informal market are 1353presented in Table 10. 1354 13555.2.1 Ownership in Formal Housing Markets 1356In the case of the formal market, age has a positive impact on the odds of owning the housing 1357solution, suggesting that older heads of household—even after controlling for income—are 1358more prone to owning a housing solution. This reflects the tendency of households in Uruguay of 1359preferring ownership to renting, and for households with older heads, the need to achieve the 1360goal of owning a home seems to get more pronounced. We also find that men are less likely than 1361women to own than to rent, which can be related to women’s increased need of a more 1362permanent housing solution as a safe haven. In fact, the predicted probability of renting increases 1363by 0.024 if the head of household is a man. 1364With respect to the source of income, public employees are more likely to rent compared 1365to wage earners in the private sector (base case), and the income source most increasing the odds 1366of ownership is owning a business. Given that these results hold even after controlling for 1367income, they may reflect differences in taste, e.g., business owners like more to “own” than 1368others, and in the predictability of income: public sector wage earners have more job security 1369since public employees cannot be terminated in Uruguay. As tenure increases, so does the 1370probability of owning a housing solution (at the mean, the ME of more than 10 years of tenure 137113721373===== PAGE 36 (score 30, numeric-paren 24, table-words 1, var-words 1) =====1374137533 1376 1377on ownership is 0.046). This may be related to the fact that the longer the tenure, the more likely 1378it is that the household will access credit in the financial market. Households where the head has 1379a partner and those with small children are more likely to own a housing unit in the formal 1380market than to rent. Indeed, being in a partnership increases the probability of owning the 1381housing solution by 0.087, and each additional child under the age of 14 adds 0.01 to the 1382probability. The explanation for this may be that non-traditional families need more stable 1383housing arrangements. Finally, households in the formal housing market who are receiving social 1384assistance are more prone to own, while those receiving remittances are more likely to rent. 1385 1386Table 8. Ownership Choices: Logit and Multinomial Logit Estimation 1387 1388Formal+ 1389 1390Own 1391 1392ME/se 1393 Perm Inc. 1394-0.000 1395 1396(0.000) 1397Age 13980.006*** 1399 1400(0.000) 1401Male (d) 1402-0.024** 1403 1404(0.009) 1405Caucasian (d) 14060.014 1407 1408(0.010) 1409Education 14100.003 1411 1412(0.006) 1413Inc. Retiree 14140.015 1415 1416(0.013) 1417Inc. Informal 14180.020* 1419 1420(0.010) 1421Inc. Own business 14220.044*** 1423 1424(0.011) 1425Inc. Public 1426-0.038** 1427 1428(0.013) 1429Inc. Other 14300.110*** 1431 1432(0.016) 1433With Partner 14340.087*** 1435 1436(0.011) 1437Divorced/Widower 1438- 1439 1440- 1441# people <14 14420.010** 1443 1444(0.004) 1445# people >14 1446- 1447 1448- 1449Reg. House. Policy (d) 1450- 1451 1452- 1453illegal electricity (d) 1454- 1455 1456- 1457Transf. (d) 1458-0.062*** 1459 1460(0.011) 1461Social assistance (d) 14620.033*** 1463 1464(0.008) 146514661467===== PAGE 37 (score 12, numeric-paren 6, table-words 2, var-words 1) =====1468146934 1470 1471 Table 8., continued 1472 1473 1474Formal+ 1475 1476Own 1477 1478ME/se 1479Tenure< 1Y (d) 1480- 1481 1482- 1483Tenure 1-5 Y(d) 1484-0.025** 1485 1486(0.011) 1487Tenure 5-10 Y(d) 1488-0.003 1489 1490(0.012) 1491Tenure>10 Y (d) 14920.046*** 1493 1494(0.009) 1495 Predicted Prob. at mean 14960.841 1497N 149813139 1499Log Likelihood 1500-5,682.583 1501Pseudo 150221503R 15040.096 1505 *p<0.1, **p<0.05, ***p<0.001 1506 ME: Marginal Effects. 1507+As per definition, formal housing arrangements only 1508offer two choices (to rent or to buy). Therefore, the 1509estimated model is a simple logit where owning is the 1510base outcome. 1511 1512Another decision within the formal market can also be assessed. Indeed, it is interesting 1513to find out whether households are ownership-biased and hence prefer to own an average or bad 1514quality housing solution that does not necessarily meet their needs rather than to rent a good 1515quality housing solution. Table 9 depicts the results of that decision. 1516At the mean, the choice with the highest predicted probability within the formal market is 1517to own a good quality housing solution, followed by owning a bad or average quality housing 1518unit. To assess if owning a bad quality solution is preferred over renting a good quality one, all 1519other things being equal, we present the results having the latter as the base outcome. As can be 1520concluded from estimation results, both owning and renting bad quality solutions are less 1521probable than renting a good quality one the higher the household’s permanent income. There is 1522no significant difference stemming from income with respect to the odds of owning vs. renting 1523high quality housing units. 1524Older heads of household are more prone to own than to rent a good quality housing unit, 1525confirming the results found earlier. Given the age structure of Uruguay’s population, this 1526confirms the earlier results regarding a certain bias toward ownership. With respect to gender, 1527the results again seem to confirm that households headed by women are more likely to own than 1528to rent given the same quality. The odds of owning a bad quality solution vs. renting a good 1529quality one are not significantly affected by gender or race. 153015311532===== PAGE 38 (score 114, numeric-paren 91, table-words 2, var-words 1) =====1533153435 1535 1536 1537Table 9. Ownership Choices: Multinomial Logit Estimation 1538 1539Estimated Coefficients 1540Marginal Effects 1541 1542Own good 1543quality 1544Own bad 1545quality 1546Rent bad 1547quality 1548Own good 1549quality 1550Own bad 1551quality 1552Rent good 1553quality 1554Rent bad 1555quality 1556 Perm. Income 1557-0.000 1558-0.000*** 1559-0.000*** 15600.000 15610.000 15620.000 15630.000 1564 1565(0.000) 1566(0.000) 1567(0.000) 1568 1569 1570 1571 1572Age 15730.052*** 15740.063*** 15750.026*** 15760.003 15770.003 1578-0.006 1579-0.001 1580 1581(0.003) 1582(0.003) 1583(0.005) 1584 1585 1586 1587 1588male (d) 1589-0.214*** 15900.106 15910.282** 1592-0.066 15930.041 15940.014 15950.011 1596 1597(0.079) 1598(0.091) 1599(0.134) 1600 1601 1602 1603 1604race_w (d) 16050.202** 16060.092 16070.155 16080.032 1609-0.012 1610-0.021 16110.000 1612 1613(0.085) 1614(0.092) 1615(0.132) 1616 1617 1618 1619 1620education_level 16210.026 16220.366*** 16230.355*** 1624-0.047 16250.052 1626-0.013 16270.008 1628 1629(0.052) 1630(0.072) 1631(0.120) 1632 1633 1634 1635 1636Inc. Retiree 16370.077 1638-0.574*** 1639-0.844*** 16400.104 1641-0.092 16420.011 1643-0.022 1644 1645(0.111) 1646(0.131) 1647(0.203) 1648 1649 1650 1651 1652Inc. Informal 16530.152 1654-0.928*** 1655-1.200*** 16560.147 1657-0.130 16580.008 1659-0.025 1660 1661(0.099) 1662(0.131) 1663(0.203) 1664 1665 1666 1667 1668Inc. Own business 16690.348*** 16701.074*** 16710.764*** 1672-0.097 16730.141 1674-0.053 16750.008 1676 1677(0.113) 1678(0.147) 1679(0.243) 1680 1681 1682 1683 1684Inc. Public 1685-0.237** 1686-0.179 16870.072 1688-0.034 16890.001 16900.025 16910.008 1692 1693(0.100) 1694(0.122) 1695(0.182) 1696 1697 1698 1699 1700Inc. Other 17011.199*** 17020.163 1703-1.567** 17040.210 1705-0.104 1706-0.078 1707-0.028 1708 1709(0.358) 1710(0.393) 1711(0.797) 1712 1713 1714 1715 1716With Partner 17170.716*** 17181.506*** 17191.194*** 1720-0.037 17210.133 1722-0.108 17230.012 1724 1725(0.089) 1726(0.111) 1727(0.175) 1728 1729 1730 1731 1732# people <14 17330.083** 17340.242*** 17350.221*** 1736-0.016 17370.026 1738-0.014 17390.003 1740 1741(0.035) 1742(0.037) 1743(0.049) 1744 1745 1746 1747 1748Transf. (d) 1749-0.443*** 1750-0.444*** 1751-0.094 1752-0.048 1753-0.015 17540.054 17550.009 1756 1757(0.076) 1758(0.087) 1759(0.123) 1760 1761 1762 1763 1764Social assistance (d) 17650.099 17660.801*** 17670.365*** 1768-0.097 17690.124 1770-0.031 17710.003 1772 1773(0.078) 1774(0.086) 1775(0.122) 1776 1777 1778 1779 1780Tenure 1-5 Y(d) 1781-0.176* 1782-0.222** 1783-0.069 1784-0.012 1785-0.012 17860.021 17870.003 1788 1789(0.093) 1790(0.105) 1791(0.140) 1792 1793 1794 1795 1796Tenure 5-10 Y(d) 1797-0.125 1798-0.019 1799-0.257 1800-0.022 18010.014 18020.012 1803-0.005 1804 1805(0.103) 1806(0.117) 1807(0.168) 1808 1809 1810 1811 1812tenure>10 Y (d) 18130.360*** 18140.263*** 1815-0.130 18160.050 1817-0.004 1818-0.035 1819-0.012 1820 1821(0.087) 1822(0.098) 1823(0.148) 1824 1825 1826 1827 1828Const. 1829-1.880*** 1830-1.118*** 1831-0.117 1832 1833 1834 1835 1836 1837(0.210) 1838(0.236) 1839(0.344) 1840 1841 1842 1843 1844Predicted Prob at mean 1845 1846 1847 18480.646 18490.196 18500.128 18510.030 1852N 185313,139 1854Log Likelihood 1855-12,598.675 1856Pseudo R2 18570.1233 1858*p<0.1, **p<0.05, ***p<0.001 1859Note: Renting a good quality solution is the base outcome. 1860 186118621863===== PAGE 39 (score 5, numeric-paren 0, table-words 1, var-words 3) =====1864186536 1866 1867The more educated the head of household, the more likely he or she will prefer to own a 1868bad/average quality housing unit than to rent a good quality one. For business owners, this 1869pattern is also supported by the results in Table 9, given that the odds of owning a bad quality 1870solution significantly increase over the odds of renting a good quality one for this subsample. 1871Again, non-traditional families, consisting of a stable partner and children—increase the 1872likelihood of preferring to own a worse housing solution than to rent a better one. Finally, less 1873tenure increases the odds of owning. This is to be expected, since tenure should be aligned with 1874an increased savings capacity. 1875As our model showed, households with certain characteristics would rather own a bad 1876quality housing unit that rent a property in better conditions. This preference is probably also a 1877byproduct of the low income stability of many households. Income uncertainty discourages 1878households that depend on salaries from entering the rental market, since they view ownership as 1879an extra safeguard in the event of a negative income shock. 1880 18815.2.2 Ownership in Informal Housing Markets 1882Within the informal market, we include new variables to understand the choice of engaging in a 1883somewhat permanent housing solution vs. a less permanent one. In fact, according to anecdotal 1884data gathered through interviews of households in the informal housing market, the motivation to 1885enter the informal market seems to be a strong driver of the type of “ownership” chosen. We 1886hence include four dummy variables accounting for the reasons why the household had to leave 1887its previous housing solution. The four reasons included are those in which the household was to 1888some extent forced to leave, namely eviction, restrictions of space, inhabitability of previous 1889solution due to structural problems, and leaving for economic reasons (negative income shock). 1890 189118921893===== PAGE 40 (score 15, numeric-paren 8, table-words 2, var-words 1) =====1894189537 1896 1897Table 10. Ownership Choices: Logit Estimation 1898(Marginal effects presented) 1899 1900 1901Permanent Arrangements 1902(illegally own) 1903Perm Inc. 1904 0.000** 1905Left - Evicted§ 1906 -0.184 1907Left - Econ§ 1908 -0.261*** 1909Left - Inhabitable§ 1910 -0.204** 1911Left - Space§ 1912 -0.165* 1913Age 1914 0.000 1915Male§ 1916 -0.039 1917White§ 1918 -0.059* 1919Education 1920 -0.084** 1921Inc. Informal§ 1922 0.016 1923With Partner§ 1924 -0.058 1925Divorced/Widower§ 1926 -0.03 1927# people <14 1928 -0.005 1929# people >14 1930 0.029** 1931Reg. House. Policy§ 1932 0.067 1933illegal electricity§ 1934 0.054* 1935Transf. § 1936 -0.015 1937Social assistance§ 1938 0.02 1939Tenure< 1Y § 1940 0.036 1941 Predicted Prob at mean 1942 0.639 1943N 1944 1054 1945Log Likelihood 1946 -661.167 1947Pseudo R2 1948 0.047 1949Note: Occupying or renting is the base outcome. 1950§ Dummy variables 1951*p<0.1, **p<0.05, ***p<0.001 1952 1953 1954Having been evicted does not significantly affect the type of arrangement chosen, while 1955those households that were indirectly forced into the new housing solution chosen are all less 1956prone to engage in a more permanent illegal housing situation. Being white and having more 1957years of education also significantly lower the odds of a more permanent illegal housing 1958solution. On the contrary, among the households in the informal housing market, income is 1959positively related to more permanent arrangements. The other two variables increasing the 1960likelihood of more permanent illegal housing arrangements are the number of adults in the 1961household and illegal access to electricity. 1962 196319641965===== PAGE 41 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1966196738 1968 19696. Policy Recommendations 1970 1971In the previous sections we analyzed the Uruguayan housing market and the determinants of 1972formality choices. We found that unlike the situation in other Latin American countries, 1973quantitative housing deficit is not a major problem, while qualitative deficit is. As described in 1974Garabato and Ramada-Sarasola (2011), one-fourth of all housing units suffer from at least one 1975form of qualitative deficit. This figure jumps to almost 60 percent when we consider households 1976in the informal market. Also, after a thorough investigation of current public housing programs, 1977we found that even though the number of programs is large, take-up for most programs is very 1978low, hampering their potential impact on housing relief. In light of these findings, this section 1979discusses possible policy options to tackle these issues. Based on the model estimated in Section 19805, we will examine how certain policy measures could impact household formality choices, 1981especially for the more vulnerable strata. 1982 19836.1 Policy Options 1984Based on the findings by Garabato and Ramada-Sarasola (2011) there seem to be two key issues 1985that need special attention from a policy perspective. The first relates to the sizeable number of 1986people that are found with informal housing arrangements and the poor housing conditions they 1987share (qualitative deficit). The second issue relates to the low mobility and high inertia found in 1988the market, probably driven by the extremely low development of the mortgage and rental 1989markets. Indeed, almost half of the households stay in the same housing unit for more than 15 1990years and almost half of these remain in the same unit for more than 30 years, irrespective of 1991changes in housing prices, or in household income, as well as of modified household 1992composition. Moreover, this inertia is not restricted to the formal market, since informal housing 1993arrangements also develop into a rather permanent housing solution, with more than 40 percent 1994of households having lived in the same housing unit for more than 10 years. While this inertia 1995can be motivated by unobservables such as idiosyncrasy and cultural elements, there may be 1996mother reasons for it that could be addressed through proper policy measures. 1997 199819992000===== PAGE 58 (score 17, numeric-paren 0, table-words 2, var-words 2) =====2001200255 2003 2004Appendix 2005 2006Appendix A. Variables and Descriptive Statistics 2007 2008Table 12. Descriptive Statistics 2009 2010Variable 2011 # of Observations 2012 Mean 2013 Std. Dev. 2014 Min 2015 Max 2016 hage 2017 21,310 2018 53.861 2019 16.780 2020 17 2021 98 2022gender 2023 21,310 2024 1.297 2025 0.457 2026 1 2027 2 2028race_b 2029 21,310 2030 0.071 2031 0.257 2032 0 2033 1 2034race_other 2035 21,310 2036 0.065 2037 0.246 2038 0 2039 1 2040race_w 2041 21,310 2042 0.864 2043 0.343 2044 0 2045 1 2046e_primaria 2047 21,310 2048 0.507 2049 0.500 2050 0 2051 1 2052e_sec 2053 21,310 2054 0.262 2055 0.440 2056 0 2057 1 2058e_tec 2059 21,310 2060 0.100 2061 0.300 2062 0 2063 1 2064e_prof 2065 21,310 2066 0.018 2067 0.135 2068 0 2069 1 2070e_univer 2071 21,310 2072 0.059 2073 0.235 2074 0 2075 1 2076e_terciario 2077 21,310 2078 0.011 2079 0.103 2080 0 2081 1 2082civilstatus_2 2083 21,310 2084 0.108 2085 0.310 2086 0 2087 1 2088civilstatus_3 2089 21,310 2090 0.529 2091 0.499 2092 0 2093 1 2094civilstatus_4 2095 21,310 2096 0.153 2097 0.360 2098 0 2099 1 2100civilstatus_5 2101 21,310 2102 0.210 2103 0.407 2104 0 2105 1 2106n_peopl_house 2107 21,310 2108 3.025 2109 1.699 2110 1 2111 19 2112n_peopl_income 2113 21,310 2114 1.898 2115 1.030 2116 0 2117 13 2118asent 2119 21,310 2120 0.055 2121 0.227 2122 0 2123 1 2124legal 2125 21,310 2126 0.945 2127 0.227 2128 0 2129 1 2130rural 2131 21,310 2132 0.199 2133 0.399 2134 0 2135 1 2136transf 2137 21,310 2138 0.135 2139 0.342 2140 0 2141 1 2142num_jobs 2143 21,310 2144 0.728 2145 0.611 2146 0 2147 11 2148one_job 2149 21,310 2150 0.583 2151 0.493 2152 0 2153 1 2154two_jobs 2155 21,310 2156 0.063 2157 0.243 2158 0 2159 1 2160multiple_job 2161 21,310 2162 0.006 2163 0.076 2164 0 2165 1 2166no_job 2167 21,310 2168 0.348 2169 0.476 2170 0 2171 1 2172aporta 2173 21,310 2174 0.436 2175 0.496 2176 0 2177 1 2178no_aporta 2179 21,310 2180 0.216 2181 0.412 2182 0 2183 1 2184rent_recibidas 2185 21,310 2186 0.090 2187 0.286 2188 0 2189 1 2190cambio_trabajo 2191 21,310 2192 0.015 2193 0.120 2194 0 2195 1 2196acc_fin 2197 21,310 2198 0.045 2199 0.208 2200 0 2201 1 2202acc_fin_publico 2203 21,310 2204 0.125 2205 0.330 2206 0 2207 1 2208ayuda_social 2209 21,310 2210 0.036 2211 0.187 2212 0 2213 1 2214demanda_housingpolicy 2215 21,310 2216 0.024 2217 0.153 2218 0 2219 1 2220tenure 2221 21,310 2222 7.893 2223 11.270 2224 0 2225 91 2226edadvivienda_1 2227 21,310 2228 0.071 2229 0.256 2230 0 2231 1 2232edadvivienda_2 2233 21,310 2234 0.136 2235 0.343 2236 0 2237 1 2238edadvivienda_3 2239 21,310 2240 0.163 2241 0.369 2242 0 2243 1 2244edadvivienda_4 2245 21,310 2246 0.131 2247 0.337 2248 0 2249 1 2250edadvivienda_5 2251 21,310 2252 0.500 2253 0.500 2254 0 2255 1 2256num_dorm 2257 21,310 2258 1.917 2259 0.981 2260 1 2261 40 2262num_ambien s 2263 21,310 2264 3.395 2265 1.652 2266 1 2267 80 2268num_banos 2269 21,310 2270 1.161 2271 0.776 2272 0 2273 21 2274sanitation 2275 21,310 2276 0.875 2277 0.331 2278 0 2279 1 2280tipovivienda_1 2281 21,310 2282 0.834 2283 0.372 2284 0 2285 1 2286tipovivienda_2 2287 21,310 2288 0.058 2289 0.235 2290 0 2291 1 2292tipovivienda_3 2293 21,310 2294 0.062 2295 0.242 2296 0 2297 1 2298tipovivienda_4 2299 21,310 2300 0.041 2301 0.198 2302 0 2303 1 2304tipovivienda_5 2305 21,310 2306 0.004 2307 0.062 2308 0 2309 1 2310tipo_construccion 2311 21,312 2312 2.496 2313 0.645 2314 1 2315 3 2316problemas 2317 21,312 2318 0.656 2319 0.475 2320 0 2321 1 2322problemas_gravess 2323 21,312 2324 0.573 2325 0.495 2326 0 2327 1 2328calidad_barrio 2329 21,312 2330 1.543 2331 0.498 2332 1 2333 2 2334ingtotalhogar 2335 21,310 2336 10,566 2337 13,252 2338 0 2339 335,000 2340owner 2341 21,312 2342 0.677 2343 0.468 2344 0 2345 1 2346tenant 2347 21,312 2348 0.128 2349 0.334 2350 0 2351 1 2352occupant 2353 21,312 2354 0.195 2355 0.396 2356 0 2357 1 235823592360===== PAGE 59 (score 7, numeric-paren 0, table-words 1, var-words 5) =====2361236256 2363 2364 2365Table 13. Description of Variables 2366 2367 2368 2369 2370 Variable name 2371 Variable label 2372 Description 2373 hage 2374 Age of Household Head 2375 Age of household head in years 2376gender 2377 Gender 2378 Gender of Household head 2379race_b 2380 Black 2381 Dummy variable that takes the value of 1 when HofH = Black 2382race_other 2383 Race Other 2384 Dummy variable that takes the value of 1 when HofH = Other 2385race_w 2386 White 2387 Dummy variable that takes the value of 1 when HofH = White 2388e_primaria 2389 Primary Education 2390 Dummy variable that takes the value of 1 when education of HofH = Primary 2391e_sec 2392 Secondary Education 2393 Dummy variable that takes the value of 1 when education of HofH = Secondary 2394e_tec 2395 Technical Education 2396 Dummy variable that takes the value of 1 when education of HofH = Technical 2397e_prof 2398 Teacher 2399 Dummy variable that takes the value of 1 when education of HofH = Teacher 2400e_univer 2401 University or above 2402 Dummy variable that takes the value of 1 when education of HofH = University or above 2403e_terciario 2404 Tertiary Education 2405 Dummy variable that takes the value of 1 when education of HofH = Tertiary non University 2406civilstatus_2 2407 Divorced 2408 Dummy variable that takes the value of 1 when HofH is divorced 2409civilstatus_3 2410 Married 2411 Dummy variable that takes the value of 1 when HofH is Married 2412civilstatus_4 2413 Widower 2414 Dummy variable that takes the value of 1 when HofH is Widower 2415civilstatus_5 2416 Single 2417 Dummy variable that takes the value of 1 when HofH is Single 2418n_peopl_house 2419 # of people in household 2420 # of people living in same household 2421n_peopl_income 2422 # of people perceiving income # of people perceiving income in household 2423asent 2424 Illegal 2425housing 2426(irregular 2427settlement) 2428 Dummy variable that takes the value of 1 when Household is part of an irregular settlement 2429legal 2430 Legal Housing 2431 Dummy variable that takes the value of 1 when Household is in a legal housing situation 2432rural 2433 Rural Areas 2434 Dummy variable that takes the value of 1 when Household is located in a rural area 2435transf 2436 Household Receives Transfers Dummy variable that takes the value of 1 when Household receives transfers 2437num_jobs 2438 # of jobs of head 2439 # of jobs of HofH 2440one_job 2441 One Job 2442 Dummy variable that takes the value of 1 when HofH has one job 2443two_jobs 2444 Two Jobs 2445 Dummy variable that takes the value of 1 when HofH has two jobs 2446multiple_job 2447 + than 2 jobs 2448 Dummy variable that takes the value of 1 when HofH has more than 2 jobs 2449no_job 2450 No job 2451 Dummy variable that takes the value of 1 when HofH is not employed 2452aporta 2453 Contribute to SS 2454 Dummy variable that takes the value of 1 when HofH is contributing to SS 2455rent_recibidas 2456 Rents perceived by household 2457 Dummy variable that takes the value of 1 when Household receives Rents 245824592460===== PAGE 64 (score 96, numeric-paren 65, table-words 1, var-words 5) =====2461246261 2463 2464Appendix B. Hedonic Price Regressions 2465 2466Table 18. Hedonic Elasticities 2467 2468 24691 24702 24713 24724 24735 24746 24757 2476 2477Coef 2478Elas2479t. 2480Coef 2481Elas2482t. 2483Coef 2484Elas2485t. 2486Coef. 2487Elas2488t. 2489Coef. 2490Elas2491t. 2492Coef. 2493Elas2494t. 2495Coef. 2496Elas2497t. 2498Not transformed 2499 # 2500Bedrooms 2501 25021.062*2503* 2504 25050.2125062 2507 25081.065*2509** 2510 25110.1825122 2513 0.604 2514 25150.0525169 2517 0.442* 2518 25190.1025203 2521 25221.252*2523** 2524 25250.1325269 2527 0.925** 2528 25290.1125306 2531 25320.1652533** 2534 25350.1625369 2537# 2538Bathroom2539s 2540 25412.147*2542** 2543 25440.4125452 2546 25471.203*2548** 2549 25500.1525516 2552 0.762* 2553 25540.0625551 2556 25570.466*2558* 2559 25600.0725617 2562 25633.863*2564** 2565 25660.3325675 2568 0.511** 2569 25700.0425718 2572 0.047 2573 25740.0325757 2576WES 2577 8.101 2578 25790.7825807 2581 -0.452 2582 -25830.0425842 2585 -2.102 2586 -25870.1225882 2589 3.535* 2590 25910.44 2592 -25936.687*2594* 2595 -25960.4325973 2598 259914.109*2600** 2601 26021.0126039 2604 26050.8892606** 2607 26080.5426096 2610Severe 2611Problems 2612 -2.607 2613 -26140.2526153 2616 -2.36* 2617 -26180.2126197 2620 -262110.1082622** 2623 -26240.5826257 2626 -26272.567*2628* 2629 -26300.32 2631 -1.823 2632 -26330.1126348 2635 1.258 2636 26370.0926381 2639 -26400.7752641** 2642 -26430.4726446 2645Medium 2646Problems 2647 -26483.108*2649** 2650 -26510.3026522 2653 -26541.168*2655* 2656 -26570.1026587 2659 0.441 2660 26610.0226626 2663 -26641.229*2665* 2666 -26670.1526683 2669 0.24 2670 26710.0126726 2673 -1.05 2674 -26750.0726766 2677 -26780.2992679** 2680 -26810.1826824 2683Small 2684Problems 2685 -26862.473*2687* 2688 -26890.24 2690 -26911.313*2692* 2693 -26940.1226951 2696 -0.03 2697 -26980.0026992 2700 -0.459 2701 -27020.0527037 2704 -0.601 2705 -27060.0327079 2708 -0.706 2709 -27100.0527111 2712 -27130.043 2714 -27150.0227166 2717Years 2718of 2719constructi2720on 2721 0.009 2722 27230.0027244 2725 -27260.569*2727* 2728 -27290.2227306 2731 -27321.029*2733* 2734 -27350.2627362 2737 -0.281 2738 -27390.1527408 2741 -27420.748*2743* 2744 -27450.2127466 2747 -0.475 2748 -27490.1527508 2751 -27520.061 2753 -27540.17 2755house_D 2756 1.244* 2757 27580.1227591 2760 27611.234*2762* 2763 27640.1127654 2766 0.666 2767 27680.0327699 2770 27711.625*2772** 2773 27740.2027752 2776 0.138 2777 27780.0027799 2780 1.79** 2781 27820.1227839 2784 27850.3082786** 2787 27880.1827899 2790Constructi2791on Type 2792 -1.37 2793 -27940.3927958 2796 27974.262*2798** 2799 28001.1628013 2802 2803 2804 1.366 2805 28060.5028071 2808 280912.0692810** 2811 28122.34 2813 -1.516 2814 -28150.3228166 2817 -28180.071 2819 -28200.13 2821Neigh. 2822Serv. 2823 0.24 2824 28250.2228263 2827 28280.796*2829** 2830 28310.6528321 2833 28340.634*2835* 2836 28370.37 2838 28390.336*2840* 2841 28420.3528435 2844 28450.401*2846* 2847 28480.2528491 2850 0.345 2851 28520.2028539 2854 28550.0742856** 2857 28580.44 2859type_neig2860h r 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875Sup. 2876durable 2877goods 2878 28791.411*2880* 2881 28820.1328837 2884 28851.277*2886* 2887 28880.1128897 2890 2** 2891 28920.1128936 2894 0.228 2895 28960.0228978 2898 28992.287*2900** 2901 29020.1429038 2904 29052.587**2906* 2907 29080.1829097 2910 29110.1652912* 2913 29140.1029151 2916Domestic 2917Service 2918 29192.872*2920** 2921 29220.2729239 2924 2.3*** 2925 29260.2129272 2928 3.64** 2929 29300.2129312 2932 0.979 2933 29340.1229352 2936 29373.024*2938** 2939 29400.1929416 2942 3.21*** 2943 29440.2329452 2946 0.128 2947 29480.0729499 2950m2 2951avg. 2952Price 2953 0.000 2954 29550.14 2956 29570.001*2958** 2959 29600.6229611 2962 0.000 2963 -29640.0229656 2966 0.000 2967 -29680.0729695 2970 29710.000*2972* 2973 29740.1629754 2976 0.000** 2977 29780.1829797 2980 29810.0002982** 2983 29840.5029851 2986Cons 2987 16.087 2988 2989 -0.438 2990 2991 33.501 2992 2993 12.656 2994 2995 -4.209 2996 2997 11.782 2998 2999 5.37 3000 3001Transformed 3002 Per. Inc. 3003 0.046 3004 30050.0630062 3007 0.019 3008 30090.0230108 3011 -0.029 3012 -30130.0430147 3015 0.045 3016 30170.0630186 3019 -0.036 3020 -30210.0530228 3023 0.073* 3024 30250.1130268 3027 30280.1393029* 3030 30310.1530323 3033Age 3034 0.115 3035 30360.0330372 3038 0.63** 3039 30400.1830411 3042 30431.092*3044** 3045 30460.2430479 3048 0.268 3049 30500.0930514 3052 30530.857*3054** 3055 30560.2130571 3058 0.611** 3059 30600.1630611 3062 30630.1943064* 3065 30660.1530671 3068Educ. 3069 0.398 3070 30710.0530723 3073 30741.217*3075* 3076 30770.1530782 3079 3.1** 3080 30810.2530829 3083 0.593 3084 30850.0930864 3087 30881.582*3089* 3090 30910.1430924 3093 0.757 3094 30950.0730965 3097 0.097 3098 30990.0631004 3101 3102 Coef 3103 3104P>|z3105| 3106 Coef 3107 3108P>|z3109| 3110 Coef 3111 3112P>|z3113| 3114 Coef 3115 3116P>|z3117| 3118 Coef 3119 3120P>|z3121| 3122 Coef 3123 3124P>|z3125| 3126 Coef 3127 3128P>|z3129| 3130 λ 3131 0.261 3132 31330.0031340 3135 0.282 3136 31370.0031380 3139 0.346 3140 31410.0031420 3143 0.255 3144 31450.0031467 3147 0.329 3148 31490.0031500 3151 0.323 3152 31530.0031540 3155 0.061 3156 31570.2731586 3159N 3160 443 3161 3162 593 3163 3164 283 3165 3166 244 3167 3168 289 3169 3170 312 3171 3172 233 3173 317431753176===== PAGE 65 (score 80, numeric-paren 61, table-words 1, var-words 5) =====3177317862 3179 3180 31811 31822 31833 31844 31855 31866 31877 3188 3189Coef 3190Elas3191t. 3192Coef 3193Elas3194t. 3195Coef 3196Elas3197t. 3198Coef. 3199Elas3200t. 3201Coef. 3202Elas3203t. 3204Coef. 3205Elas3206t. 3207Coef. 3208Elas3209t. 3210LR 321123212χ3213 3214 238.14 3215 3216 317.33 3217 3218 92.10 3219 3220 99.00 3221 3222 210.67 3223 3224 156.00 3225 3226 3227130.532283 3229 3230Prob> 323123232χ3233 3234 0.000 3235 3236 0.000 3237 3238 0.000 3239 3240 0.000 3241 3242 0.000 3243 3244 0.000 3245 3246 0.000 3247 3248λ =-1 3249 0.000 3250 3251 0.000 3252 3253 0.000 3254 3255 0.000 3256 3257 0.000 3258 3259 0.000 3260 3261 0.000 3262 3263λ =0 3264 0.000 3265 3266 0.000 3267 3268 0.000 3269 3270 0.004 3271 3272 0.000 3273 3274 0.000 3275 3276 0.273 3277 3278λ =1 3279 0.000 3280 3281 0.000 3282 3283 0.000 3284 3285 0.000 3286 3287 0.000 3288 3289 0.000 3290 3291 0.000 3292 3293*p<0.1, **p<0.05, ***p<0.001 3294 3295Table 19. Hedonic Elasticities (continued) 3296 3297 32988 32999 330010 330111† 330212 330313 330414 3305 3306 Coef. 3307 3308Elas3309t. 3310 Coef. 3311 3312Ela3313st. 3314 Coef. 3315 3316Ela3317st. 3318 Coef. 3319 3320Ela3321st. 3322 Coef. 3323 3324Ela3325st. 3326 Coef. 3327 3328Ela3329st. 3330 Coef. 3331 3332Ela3333st. 3334Not transformed 3335# 3336Bedrooms 3337 33381.483*3339* 3340 33410.1334203 3343 2.048 3344 33450.0334668 3347 -0.034 3348 -33490.0335004 33510.124*3352** 3353 3354 33551.174*3356** 3357 33580.2335942 3360 33614.088*3362** 3363 33640.1336577 3366 0.104 3367 33680.0336935 3370# 3371Bathroom3372s 3373 33742.594*3375* 3376 33770.1337816 3379 33801.983*3381* 3382 33830.0338445 3385 33865.897*3387** 3388 33890.3339067 3391 0.011 3392 3393 33940.652*3395* 3396 33970.0339879 3399 0.683 3400 34010.0340219 3403 34040.285* 3405 34060.0340755 3408WES 3409 34109.041*3411* 3412 34130.3341447 3415 341620.8993417** 3418 34190.334207 3421 34223.104*3423* 3424 34250.1342684 3427 34280.304*3429* 3430 3431 34321.922*3433* 3434 34350.2343609 3437 343812.2183439** 3440 34410.2344296 3443 1.134 3444 34450.1344694 3447Severe 3448Problems 3449 -34506.485* 3451 -34520.2345349 3454 -345512.8023456** 3457 -34580.2345927 3460 -34612.678* 3462 -34630.1346459 3465 -34660.238* 3467 3468 -34691.198*3470* 3471 -34720.134733 3474 -34758.904* 3476 -34770.2347815 3479 -34801.49** 3481 -34820.2348355 3484Medium 3485Problems 3486 -34876.609*3488** 3489 -34900.2349154 3492 -34938.072*3494* 3495 -34960.1349743 3498 -1.369 3499 -35000.0350181 3502 -35030.2*** 3504 3505 -35061.045*3507* 3508 -35090.1351014 3511 -35127.321*3513** 3514 -35150.1351677 3517 -0.58* 3518 -35190.0352099 3521Small 3522Problems 3523 -35242.183* 3525 -35260.0352784 3528 -5.056 3529 -35300.035319 3532 -0.172 3533 -35340.035351 3536 -35370.095* 3538 3539 -0.087 3540 -35410.0354209 3543 -3.151 3544 -35450.0354676 3547 -35480.879*3549* 3550 -35510.1355251 3553Years of 3554construuc3555tion 3556 0.547 3557 35580.0355993 3560 0.11 3561 35620.0356308 3564 -0.403 3565 -35660.0356783 3568 0.001 3569 3570 35710.236* 3572 35730.1357405 3575 -0.474 3576 -35770.0357849 3579 0.013 3580 35810.0358209 3583House 3584 35855.034*3586** 3587 35880.1358993 3590 35917.615*3592* 3593 35940.1359535 3596 35972.334*3598* 3599 36000.1360138 3602 36030.128*3604* 3605 3606 36071.17** 3608 36090.1361027 3611 36124.78** 3613 36140.1361516 3616 -0.081 3617 -36180.0361914 3620Construct3621ion Type 3622 36233.945*3624* 3625 36260.4362736 3628 362910.9893630*** 3631 36320.5363343 3634 36352.323*3636* 3637 36380.3363957 3640 36410.421*3642** 3643 3644 36451.151*3646* 3647 36480.3364938 3650 36516.972*3652* 3653 36540.4365573 3656 36572.301*3658** 3659 36601.0366195 3662Neigh. 3663Serv. 3664 36651.445*3666* 3667 36680.4366938 3670 1.916 3671 36720.2367315 3674 36751.143*3676** 3677 36780.3367921 3680 36810.044*3682* 3683 3684 36850.594*3686** 3687 36880.4368901 3690 36911.533*3692* 3693 36940.2369579 3696 -0.079 3697 -36980.0369983 3700Type 3701Neigh. 3702 0.161 3703 37040.037052 3706 -1.909 3707 -37080.1370941 3710 37111.189*3712** 3713 -37140.3371532 3716 3717 3718-37190.766*3720** 3721 -37220.4372343 3724 -37253.629*3726* 3727 -37280.437295 3730 0.543 3731 37320.5373367 3734Sup. 3735durable 3736goods 3737 1.301 3738 37390.037405 3741 37429.904*3743* 3744 37450.1374676 3747 0.534 3748 37490.0375032 3751 37520.167*3753* 3754 3755 37561.085*3757* 3758 37590.1376018 3761 37624.888*3763* 3764 37650.1376618 3767 0.446 3768 37690.0377076 3771Domestic 3772Service 3773 37743.608* 3775 37760.1377739 3778 377912.8443780* 3781 37820.2378328 3784 -0.986 3785 -37860.0378758 3788 0.133 3789 3790 1.302 3791 37920.1379342 3794 0.856 3795 37960.0379721 3798 1.428 3799 38000.2380145 3802m2 3803avg. 3804Price 3805 -0.006 3806 -38071.438084 3809 0.003 3810 38110.3381228 3813 38148.95E-381505 3816 -38170.1381818 3819 3820 3821 3822 -38230.138241 3825 38260.002*3827* 3828 38290.2383031 3831 3832 383338343835===== PAGE 66 (score 66, numeric-paren 46, table-words 1, var-words 1) =====3836383763 3838 3839 38408 38419 384210 384311† 384412 384513 384614 3847 3848 Coef. 3849 3850Elas3851t. 3852 Coef. 3853 3854Ela3855st. 3856 Coef. 3857 3858Ela3859st. 3860 Coef. 3861 3862Ela3863st. 3864 Coef. 3865 3866Ela3867st. 3868 Coef. 3869 3870Ela3871st. 3872 Coef. 3873 3874Ela3875st. 3876Cons 3877 387841.092 3879 3880 -5.871 3881 3882 388319.688 3884 3885 4.331 3886 3887 388815.107 3889 3890 389113.324 3892 3893 3.668 3894 3895Transformed 3896 Perm. 3897Inc. 3898 0.046 3899 39000.0390187 3902 39030.109*3904* 3905 39060.2390758 3908 39090.141*3910** 3911 39120.2391364 3914 0.099 3915 3916 39170.056* 3918 39190.0392092 3921 39220.063*3923* 3924 39250.1392639 3927 39280.168*3929** 3930 39310.2393249 3933Age 3934 39350.886*3936* 3937 39380.1393973 3940 0.532 3941 39420.0394375 3944 -0.008 3945 -39460.0394702 3948 0.092 3949 3950 0.265 3951 39520.0395391 3954 39551.486*3956** 3957 39580.2395941 3960 39610.345* 3962 39630.1396447 3965Education 3966 2.536 3967 39680.1396942 3970 -39715.761* 3972 -39730.1397455 3975 -0.393 3976 -39770.039783 3979 0.05 3980 3981 0.466 3982 39830.0398463 3985 1.165 3986 39870.0398842 3989 -0.396 3990 -39910.0399282 3993 3994 Coef 3995 3996P>|3997z| 3998 Coef 3999 4000P>|4001z| 4002 Coef 4003 4004P>|4005z| 4006 Coef 4007 4008P>|4009z| 4010 Coef 4011 4012P>|4013z| 4014 Coef 4015 4016P>|4017z| 4018 Coef 4019 4020P>|4021z| 4022 λ 4023 0.405 4024 40250.0402600 4027 0.513 4028 40290.0403000 4031 0.366 4032 40330.0403400 4035 4036 4037 0.287 4038 40390.0404000 4041 0.474 4042 40430.0404400 4045 0.228 4046 40470.0404800 4049N 4050 295 4051 4052 256 4053 4054 228 4055 4056 211 4057 4058 460 4059 4060 331 4061 4062 248 4063 4064LR 406524066χ4067 4068 4069203.85 4070 4071 181.73 4072 4073 4074162.27 4075 4076 13.31 4077 4078 4079324.85 4080 4081 4082199.92 4083 4084 158.4 4085 4086Prob> 408724088χ4089 4090 0.000 4091 4092 0.000 4093 4094 0.000 4095 4096 0.000 4097 4098 0.000 4099 4100 0.000 4101 4102 0.000 4103 4104λ =-1 4105 0.000 4106 4107 0.000 4108 4109 0.000 4110 4111 4112 4113 0.000 4114 4115 0.000 4116 4117 0.000 4118 4119λ =0 4120 0.000 4121 4122 0.000 4123 4124 0.000 4125 4126 4127 4128 0.000 4129 4130 0.000 4131 4132 0.000 4133 4134λ =1 4135 0.000 4136 4137 0.000 4138 4139 0.000 4140 4141 4142 4143 0.000 4144 4145 0.000 4146 4147 0.000 4148 4149*p<0.1, **p<0.05, ***p<0.001. + indicates fitted model is log linear. In these cases, elasticities are estimated coefficients and model significance 4150was tested with and F-test and not a 415124152χ4153 distribution 4154 4155Table 20. Hedonic Elasticities (continued) 4156 4157 415815 415916 416017 416118 416219 416320 416421† 4165 4166Coef. 4167Elas4168t. 4169Coef. 4170Elas4171t. 4172Coef. 4173Elas4174t. 4175Coef. 4176Elas4177t. 4178Coef. 4179Elas4180t. 4181Coef. 4182Elas4183t. 4184Coef. 4185Elas4186t. 4187Not transformed 4188 # 4189Bedroo4190ms 4191 41920.775*4193* 4194 41950.1241964 4197 0.27** 4198 41990.1242009 4201 42020.441*4203* 4204 42050.0942069 4207 0.356 4208 42090.0542108 4211 42122.182*4213* 4214 42150.1342161 4217 1.905* 4218 42190.1042207 4221 42220.067*4223* 4224 4225# 4226Bathroo4227ms 4228 42291.607*4230* 4231 42320.1542332 4234 42350.285*4236* 4237 42380.08 4239 0.192 4240 42410.0242425 4243 42442.993*4245** 4246 42470.27 4248 1.049 4249 42500.0342515 4252 1.787* 4253 42540.0542557 4256 42570.251*4258* 4259 4260WES 4261 42621.693*4263* 4264 42650.1442669 4267 42680.716*4269* 4270 42710.1842725 4273 42741.394*4275* 4276 42770.1642788 4279 42802.098*4281* 4282 42830.1842847 4285 42865.275*4287* 4288 42890.1642909 4291 2.164 4292 42930.0642946 4295 42960.254*4297** 4298 4299Severe 4300Prob-4301lems 4302 -43031.859*4304* 4305 -43060.1643074 4308 -43091.055*4310** 4311 -43120.2743133 4314 -43152.542*4316** 4317 -43180.3043196 4320 -43211.967*4322* 4323 -43240.1743255 4326 -43275.948*4328* 4329 -43300.1943311 4332 -43336.32** 4334 -43350.1943363 4337 -0.099 4338 4339Medium 4340Prob-4341lems 4342 -43431.42** 4344 -43450.1243465 4347 -43480.574*4349* 4350 -43510.1443528 4353 -43541.501*4355** 4356 -43570.1843581 4359 -43601.623*4361* 4362 -43630.1443644 4365 -2.487 4366 -43670.08 4368 -43693.704* 4370 -43710.1143723 4373 -0.024 4374 4375Small 4376Prob-4377lems 4378 -0.573 4379 -43800.0543811 4382 -0.31* 4383 -43840.08 4385 -43860.954* 4387 -43880.1143895 4390 -0.453 4391 -43920.04 4393 -2.556 4394 -43950.0843962 4397 -1.245 4398 -43990.0344008 4401 0.055 4402 4403Years of 4404Construc4405t-ion 4406 -0.082 4407 -44080.03 4409 -0.036 4410 -44110.0344127 4413 -0.158 4414 -44150.0744162 4417 0.157 4418 44190.0444207 4421 -0.573 4422 -44230.0644242 4425 0.721 4426 44270.0844288 4429 0.008 4430 4431House 4432 0.896* 4433 44340.074435 44360.635*4437 44380.164439 44401.742*4441 44420.21 4443 44441.537*4445 44460.134447 44485.428*4449 44500.174451 44529.651*4453 44540.294455 0.144 4456 445744584459===== PAGE 67 (score 57, numeric-paren 41, table-words 0, var-words 0) =====4460446164 4462 44639 4464* 44654 4466** 4467* 44687 4469* 44704 4471** 44725 4473 447415 447516 447617 447718 447819 447920 448021† 4481 4482Coef. 4483Elas4484t. 4485Coef. 4486Elas4487t. 4488Coef. 4489Elas4490t. 4491Coef. 4492Elas4493t. 4494Coef. 4495Elas4496t. 4497Coef. 4498Elas4499t. 4500Coef. 4501Elas4502t. 4503Construc4504-tion 4505Type 4506 45072.585*4508** 4509 45100.6145114 4512 45130.613*4514** 4515 45160.4145177 4518 45190.819*4520* 4521 45220.24 4523 45241.131*4525* 4526 45270.2345286 4529 45303.207*4531** 4532 45330.2545342 4535 45366.114*4537** 4538 45390.4845406 4541 45420.214*4543** 4544 4545Neigh. 4546Serv. 4547 0.377* 4548 45490.1945504 4551 45520.202*4553* 4554 45550.2445566 4557 45580.426*4559** 4560 45610.2645628 4563 45640.652*4565* 4566 45670.2445688 4569 0.529 4570 45710.0645728 4573 45742.712*4575** 4576 45770.4445785 4579 45800.065*4581* 4582 4583Type 4584Neigh. 4585 -45860.942*4587* 4588 -45890.5145903 4591 -45920.511*4593* 4594 -45950.8045966 4597 -0.509 4598 -45990.4446002 4601 -46021.684*4603* 4604 -46051.0846066 4607 -46087.093*4609** 4610 -46111.7146124 4613 -46143.835* 4615 -46160.8346178 4618 4619 4620Sup. 4621durable 4622goods 4623 46242.035*4625** 4626 46270.18 4628 46290.455*4630* 4631 46320.1146338 4634 46351.489*4636** 4637 46380.1746399 4640 0.703 4641 46420.0646433 4644 46453.232*4646* 4647 46480.1046494 4650 46515.233*4652* 4653 46540.16 4655 46560.131*4657** 4658 4659Domesti4660c Service 4661 1.112 4662 46630.0946648 4665 0.114 4666 46670.0246689 4669 2.271* 4670 46710.2746724 4673 -1.524 4674 -46750.1346766 4677 -5.745 4678 -46790.1846804 4681 468224.8244683** 4684 46850.7546868 4687 -46880.192*4689* 4690 4691m2 avg. 4692Price 4693 46940.001*4695* 4696 46970.3746984 4699 0 4700 47010.0847022 4703 47040.001*4705* 4706 47070.2547082 4709 -0.001 4710 -47110.3047126 4713 -0.002 4714 -47150.2047165 4717 0.004 4718 47190.3847203 4721 4722 4723Cons 4724 13.408 4725 4726 10.77 4727 4728 15.885 4729 4730 28.418 4731 4732 85.532 4733 4734 23.232 4735 4736 2.473 4737 4738Transformed 4739 Perm. 4740Inc. 4741 0.037 4742 47430.0647442 4745 47460.119*4747* 4748 47490.1647502 4751 0.063* 4752 47530.1047546 4755 47560.095*4757* 4758 47590.1747604 4761 47620.072*4763* 4764 47650.1747663 4767 0.051* 4768 47690.1147704 4771 47720.271*4773** 4774 4775Age 4776 47770.584*4778* 4779 47800.1847811 4782 0.202* 4783 47840.1047856 4786 0.264 4787 47880.0947897 4790 0.167 4791 47920.0547933 4794 0.594* 4795 47960.1147978 4798 0.483 4799 48000.0948011 4802 0.154* 4803 4804Educatio4805n 4806 0.083 4807 48080.0048099 4810 -0.007 4811 -48120.0048132 4814 0.417 4815 48160.0648171 4818 -0.031 4819 -48200.0048213 4822 1.129 4823 48240.0448259 4826 0.602 4827 48280.0248295 4830 -48310.145* 4832 4833 4834 Coef 4835 4836P>|z4837| 4838 Coef 4839 4840P>|z4841| 4842 Coef 4843 4844P>|z4845| 4846 Coef 4847 4848P>|z4849| 4850 Coef 4851 4852P>|z4853| 4854 Coef 4855 4856P>|z4857| 4858 Coef 4859 4860P>|z4861| 4862 λ 4863 0.315 4864 48650.0048660 4867 0.178 4868 48690.0048701 4871 0.285 4872 48730.0048740 4875 0.325 4876 48770.0048780 4879 0.466 4880 48810.0048820 4883 0.461 4884 48850.0048860 4887 4888 4889 N 4890 527 4891 4892 451 4893 4894 487 4895 4896 441 4897 4898 515 4899 4900 288 4901 4902 472 4903 4904LR 490524906χ4907 4908 283.91 4909 4910 259.36 4911 4912 270.64 4913 4914 225.69 4915 4916 202.11 4917 4918 193.52 4919 4920 16.17 4921 4922Prob> 492324924χ4925 4926 0.000 4927 4928 0.000 4929 4930 0.000 4931 4932 0.000 4933 4934 0.000 4935 4936 0.000 4937 4938 0.000 4939 4940λ =-1 4941 0.000 4942 4943 0.000 4944 4945 0.000 4946 4947 0.000 4948 4949 0.000 4950 4951 0.000 4952 4953 4954 4955λ =0 4956 0.000 4957 4958 0.001 4959 4960 0.000 4961 4962 0.000 4963 4964 0.000 4965 4966 0.000 4967 4968 4969 4970λ =1 4971 0.000 4972 4973 0.000 4974 4975 0.000 4976 4977 0.000 4978 4979 0.000 4980 4981 0.000 4982 4983 4984 4985 *p<0.1, **p<0.05, ***p<0.001 4986+ indicates fitted model is log linear. In these cases, elasticities are estimated coefficients and model significance was tested with an F-test and 4987not a 498824989χ4990 distribution 4991 4992 499349944995===== PAGE 68 (score 83, numeric-paren 62, table-words 1, var-words 6) =====4996499765 4998 4999Table 21. Hedonic Elasticities (continued) 5000 5001 5002 22† 5003 24† 5004 26† 5005 28 5006 29 5007 31† 5008 32 5009 5010 Coef. 5011 5012Elas5013t. 5014 Coef. 5015 5016Elas5017t. 5018 Coef. 5019 5020Elas5021t. 5022 Coef. 5023 5024Elas5025t. 5026 Coef. 5027 5028Elas5029t. 5030 Coef. 5031 5032Elas5033t. 5034 Coef. 5035 5036Elas5037t. 5038Not transformed 5039# 5040Bedrooms 5041 50420.075*5043** 5044 5045 0.022 5046 5047 50480.092*5049** 5050 5051 50520.889*5053* 5054 50550.1350567 5057 50580.116*5059** 5060 50610.12 5062 50630.0965064** 5065 5066 50670.411*5068** 5069 50700.1650717 5072 5073 22† 5074 24† 5075 26† 5076 28 5077 29 5078 31† 5079 32 5080 5081 Coef. 5082 5083Elas5084t. 5085 Coef. 5086 5087Elas5088t. 5089 Coef. 5090 5091Elas5092t. 5093 Coef. 5094 5095Elas5096t. 5097 Coef. 5098 5099Elas5100t. 5101 Coef. 5102 5103Elas5104t. 5105 Coef. 5106 5107Elas5108t. 5109# 5110Bathroom5111s 5112 51130.277*5114** 5115 5116 51170.272*5118* 5119 5120 51210.052*5122** 5123 5124 0.241 5125 51260.0251271 5128 51290.107*5130** 5131 51320.0651334 5134 51350.0835136* 5137 5138 51390.365*5140** 5141 51420.0851435 5144WES 5145 51460.197*5147** 5148 5149 51500.297*5151** 5152 5153 51540.22**5155* 5156 5157 51583.474*5159** 5160 51610.2851624 5163 51640.465*5165** 5166 51670.2451684 5169 51700.1745171** 5172 5173 51740.991*5175** 5176 51770.1951789 5179Severe 5180Problems 5181 -51820.195*5183** 5184 5185 -51860.285*5187* 5188 5189 -51900.245*5191** 5192 5193 -0.52 5194 -51950.0451962 5197 -51980.371*5199** 5200 -52010.1952025 5203 -52040.068 5205 5206 -52071.002*5208** 5209 -52100.2052111 5212Medium 5213Problems 5214 -52150.089*5216* 5217 5218 -0.071 5219 5220 -52210.081*5222* 5223 5224 -1.066 5225 -52260.0852277 5228 -0.076 5229 -52300.04 5231 0.039 5232 5233 -52340.686*5235** 5236 -52370.1352388 5239Small 5240Problems 5241 -0.013 5242 5243 -0.12 5244 5245 -0.024 5246 5247 0.004 5248 0 5249 -0.009 5250 -52510.0052524 5253 0.025 5254 5255 -52560.352*5257* 5258 -52590.0752601 5261Years 5262of 5263Const. 5264 0 5265 5266 0.003 5267 5268 52690.022*5270* 5271 5272 0.361 5273 52740.1152758 5276 -0.011 5277 -52780.0252794 5280 -52810.012 5282 5283 0.038 5284 52850.0252869 5287House 5288 0.073 5289 5290 -0.011 5291 5292 52930.084*5294* 5295 5296 -4.307 5297 -52980.3552992 5300 53010.236*5302* 5303 53040.1253054 5306 53070.1675308* 5309 5310 53111.283*5312** 5313 53140.2553158 5316Constructi5317on Type 5318 53190.172*5320** 5321 5322 53230.177*5324** 5325 5326 53270.17**5328* 5329 5330 53311.557*5332* 5333 53340.27 5335 53360.397*5337** 5338 53390.5353405 5341 53420.1015343** 5344 5345 53460.604*5347** 5348 53490.3053507 5351Neigh. 5352Serv. 5353 53540.065*5355** 5356 5357 0.016 5358 5359 53600.096*5361** 5362 5363 -0.19 5364 -53650.0653662 5367 53680.149*5369** 5370 53710.4453722 5373 53740.0755375** 5376 5377 53780.364*5379** 5380 53810.4353821 5383Type 5384Neigh. 5385 -53860.066*5387* 5388 5389 -0.009 5390 5391 -53920.028*5393* 5394 5395 5396 5397 -53980.217*5399** 5400 -54010.2554024 5403 5404 5405 -54060.263*5407** 5408 -54090.1254107 5411Luxury 5412goods 5413 54140.18**5415* 5416 5417 54180.275*5419** 5420 5421 54220.167*5423** 5424 5425 54261.677*5427* 5428 54290.1354307 5431 54320.292*5433** 5434 54350.1554363 5437 54380.2245439** 5440 5441 54420.776*5443** 5444 54450.1554466 5447Domestic 5448Service 5449 54500.178*5451* 5452 5453 0.158 5454 5455 54560.332*5457** 5458 5459 54604.326*5461* 5462 54630.3554644 5465 54660.547*5467** 5468 54690.2854707 5471 54720.4185473** 5474 5475 54761.801*5477** 5478 54790.3654802 5481m2 5482avg. 5483Price 5484 5485 5486 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496 5497 5498Cons 5499 4.181 5500 5501 5.052 5502 5503 3.562 5504 5505 13.42 5506 5507 6.768 5508 5509 3.982 5510 5511 5.742 5512 551355145515===== PAGE 69 (score 19, numeric-paren 14, table-words 0, var-words 0) =====5516551766 5518 5519 5520 5521 Coef. 5522 5523Elas5524t. 5525 5526Coef. 5527 5528Elast. 5529 Coef. 5530 5531Elast5532. 5533 Coef. 5534 5535Elast5536. 5537 Coef. 5538 5539Elast5540. 5541 Coef. 5542 5543Elas5544t. 5545 Coef. 5546 5547El5548as5549t. 5550Transformed 5551 Perm. 5552Inc. 5553 55540.108*5555* 5556 5557 55580.0655595 5560 5561 55620.182**5563* 5564 5565 55660.079** 5567 55680.1655694 5570 0.04 5571 55720.0455735 5574 55750.154*5576* 5577 5578 55790.12*** 5580 55810.167 5582Age 5583 55840.23**5585* 5586 5587 55880.1055891 5590 5591 55920.19*** 5593 5594 55950.886**5596* 5597 55980.3055992 5600 56010.183*5602* 5603 56040.1356055 5606 0.097 5607 5608 56090.494**5610* 5611 0.23 5612Educatio5613n 5614 56150.084* 5616 5617 56180.0456195 5620 5621 0.048 5622 5623 1.1 5624 56250.1156261 5627 56280.164*5629* 5630 56310.0956321 5633 0.048 5634 5635 56360.561** 5637 56380.132 5639 5640 Coef 5641 5642P>|z5643| 5644 5645Coef 5646 5647P>|z5648| 5649 Coef 5650 5651P>|z5652| 5653 Coef 5654 5655P>|z| 5656 Coef 5657 5658P>|z| 5659 Coef 5660 5661P>|z5662| 5663 Coef 5664 P>|z| 5665 λ 5666 5667 5668 5669 5670 5671 5672 0.355 5673 56740.0056750 5676 0.085 5677 56780.0056790 5680 5681 5682 0.210 5683 56840.000 5685 N 5686 925 5687 5688 354 5689 5690 1501 5691 5692 413 5693 5694 882 5695 5696 444 5697 5698 1598 5699 5700LR 570125702χ5703 5704 68.73 5705 5706 17.3 5707 5708 139.16 5709 5710 140.95 5711 5712 714.8 5713 5714 8.6 5715 5716 57171082.32 5718 5719Prob>572025721χ5722 5723 0.000 5724 5725 57260.0057270 5728 5729 0.000 5730 5731 0.000 5732 5733 0.000 5734 5735 0.000 5736 5737 0.000 5738 5739λ =-1 5740 5741 5742 5743 5744 5745 5746 0.000 5747 5748 0.000 5749 5750 5751 5752 0.000 5753 5754λ =0 5755 5756 5757 5758 5759 5760 5761 0.000 5762 5763 0.015 5764 5765 5766 5767 0.000 5768 5769λ =1 5770 5771 5772 5773 5774 5775 5776 0.000 5777 5778 0.000 5779 5780 5781 5782 0.000 5783 5784 5785*p<0.1, **p<0.05, ***p<0.001 5786+ indicates fitted model is log linear. In these cases, elasticities are estimated coefficients and model significance was tested with and F-test and 5787not a 578825789χ5790 distribution 5791 579257935794===== PAGE 70 (score 90, numeric-paren 65, table-words 1, var-words 6) =====5795579667 5797 5798Table 22. Hedonic Elasticities (continued) 5799 5800 5801 34† 5802 35 5803 36 5804 37† 5805 38† 5806 39 5807 5808 Coef. 5809 5810Elast. 5811 Coef. 5812 5813Elast. 5814 Coef. 5815 5816Elast. 5817 Coef. 5818 5819Elast. 5820 Coef. 5821 5822Elast. 5823 Coef. 5824 5825Elast. 5826Not transformed 5827# 5828Bedrooms 5829 0.044 5830 5831 6.852** 5832 58330.111 5834 0.219** 5835 58360.141 5837 58380.074*** 5839 5840 0.058** 5841 5842 0.449 5843 58440.084 5845# 5846Bathrooms 5847 58480.339*** 5849 5850 24.946** 5851 58520.215 5853 58540.514*** 5855 58560.193 5857 58580.126*** 5859 5860 58610.093*** 5862 5863 2.208** 5864 58650.245 5866WES 5867 0.247* 5868 5869 9.928 5870 58710.082 5872 0.499** 5873 58740.167 5875 58760.279*** 5877 5878 0.186** 5879 5880 1.913** 5881 58820.196 5883Severe 5884Problems 5885 -0.171 5886 5887 -5.351 5888 -58890.044 5890 -58911.203*** 5892 -58930.402 5894 -58950.271*** 5896 5897 -0.152* 5898 5899 -59002.666** 5901 -59020.273 5903Medium 5904Problems 5905 -59060.171** 5907 5908 -590920.104** 5910 -59110.166 5912 -59130.342** 5914 -59150.114 5916 -59170.127*** 5918 5919 -0.072 5920 5921 -1.24** 5922 -59230.127 5924Small 5925Problems 5926 0.042 5927 5928 -592917.392** 5930 -59310.144 5932 -0.262 5933 -59340.088 5935 -59360.117** 5937 5938 -0.045 5939 5940 -0.835 5941 -59420.085 5943Years 5944of 5945const. 5946 0 5947 5948 4.796** 5949 59500.146 5951 0.021 5952 59530.024 5954 0.015 5955 5956 0.016 5957 5958 -0.359* 5959 -59600.149 5961House 5962 0.217 5963 5964 1.718 5965 59660.014 5967 -0.063 5968 -59690.021 5970 59710.249*** 5972 5973 -0.035 5974 5975 59763.468*** 5977 59780.354 5979Const. 5980Type 5981 0.195** 5982 5983 598417.097*** 5985 59860.358 5987 59880.636*** 5989 0.5 5990 59910.104*** 5992 5993 59940.168*** 5995 5996 59971.559*** 5998 59990.388 6000Neigh. 6001Serv. 6002 0.037 6003 6004 6.963*** 6005 60060.248 6007 0.003 6008 60090.004 6010 60110.039*** 6012 6013 60140.064*** 6015 6016 60171.074*** 6018 60190.549 6020Type 6021Neigh. 6022 -60230.148*** 6024 6025 -5.019** 6026 -0.1 6027 0.005 6028 6029 -60300.035** 6031 6032 -60330.034** 6034 6035 -0.285 6036 6037Sup. 6038durable 6039goods 6040 0.11 6041 6042 12.301** 6043 60440.102 6045 60460.637*** 6047 60480.213 6049 60500.136*** 6051 6052 60530.143*** 6054 6055 0.877* 6056 0.09 6057Domestic 6058Service 6059 0.259** 6060 6061 -3.014 6062 -60630.025 6064 0.192 6065 60660.064 6067 0.14** 6068 6069 60700.347*** 6071 6072 2.877** 6073 60740.294 6075m2 6076avg. 6077Price 6078 6079 6080 6081 6082 6083 6084 6085 6086 6087 6088 6089 6090Cons 6091 4.876 6092 6093 10.882 6094 6095 6.102 6096 6097 4.171 6098 6099 5.408 6100 6101 6.102 6102 6103Transformed 6104 Perm. Inc. 6105 0.235** 6106 6107 0.042** 6108 61090.113 6110 61110.171*** 6112 0.22 6113 61140.171*** 6115 6116 0.084* 6117 6118 0.029 6119 61200.048 6121Age 6122 -0.096 6123 6124 1.691** 6125 61260.172 6127 0.143 6128 61290.086 6130 0.17** 6131 6132 0.062 6133 6134 0.841** 6135 0.29 6136Education 6137 0.054 6138 6139 9.246* 6140 61410.118 6142 0.671** 6143 61440.246 6145 0.068 6146 6147 61480.268*** 6149 6150 0.978 6151 61520.123 6153 6154 Coef 6155 P>|z| 6156 Coef 6157 P>|z| 6158 Coef 6159 P>|z| 6160 Coef 6161 P>|z| 6162 Coef 6163 P>|z| 6164 Coef 6165 P>|z| 6166 λ 6167 6168 6169 0.632 6170 61710.000 6172 0.147 6173 61740.000 6175 6176 6177 6178 6179 0.303 6180 61810.000 6182 N 6183 228 6184 6185 484 6186 6187 719 6188 6189 680 6190 6191 728 6192 6193 376 6194 6195LR 619626197χ6198 6199 25.15 6200 6201 223.17 6202 6203 323.73 6204 6205 41.25 6206 6207 31.3 6208 6209 224.37 6210 6211Prob> 621226213χ6214 6215 0.000 6216 6217 0.000 6218 6219 0.000 6220 6221 0.000 6222 6223 0.000 6224 6225 0.000 6226 6227λ =-1 6228 6229 6230 0.000 6231 6232 0.000 6233 6234 6235 6236 6237 6238 0.000 6239 6240λ =0 6241 6242 6243 0.000 6244 6245 0.000 6246 6247 6248 6249 6250 6251 0.000 6252 6253λ =1 6254 6255 6256 0.000 6257 6258 0.000 6259 6260 6261 6262 6263 6264 0.000 6265 6266*p<0.1, **p<0.05, ***p<0.001 6267+ indicates fitted model is log linear. In these cases, elasticities are estimated coefficients and model significance was tested with and F-test and 6268not a 626926270χ6271 distribution 6272 6273 6274