1# RECORD 85ec88163a881d2f9a23ced6110d5e4e05b48d092# TITLE: Urban Traffic Arterials and the Sale Price of Single Family Houses in Portland: A Hedonic Analysis of Impacts3# YEAR: 2022.0 VENUE: nan DOI: 10.15760/etd.80614# AUTHORS: P. Wuest5# PAGES: 84 CANDIDATE TABLE PAGES (1-based): [28, 46, 47, 49]6# ABSTRACT: An abstract of the thesis of Philip Wuest for the Master of Urban Studies degree presented August 6, 1997. Title: Urban Traffic Arterials and the Sale Price of Single Family Houses in Portland: A Hedonic Analysis of Impacts. This thesis explores the hypothesis that the prices of single family houses are negatively influenced by proximity to urban arterial roadways. A hedonic price.model is developed that tests the effects of traffic volumes and proximity on sale price. Using a data set of sale records from Portland Oregon in 1994, a traditional hedonic model is developed based on housing and neighborhood characteristics. Using a geographic information system, each sale record is associated with the nearest arterial roadway, and the data on distance to the roadway and level of traffic is returned to the sale record. These data are then applied in the hedonic equation. The results indicate that distance to an arterial roadway has a much larger effect than traffic volume alone. It is also found that the effects of high traffic arterials diminish rapidly with distance, becoming difficult to distinguish beyond about 2/3 of a city block. The estimated effect on sale price of close proximity to a busy roadway is large, approximately -10% of sale price. 2 URBAN TRAFFIC ARTERIALS AND THE SALE PRICE OF SINGLE FAMILY HOUSES IN PORTLAND: A HEDONIC ANALYSIS OF IMPACTS789===== PAGE 1 (score 4, numeric-paren 0, table-words 0, var-words 4) =====1011Portland State University 12Portland State University 13PDXScholar 14PDXScholar 15Dissertations and Theses 16Dissertations and Theses 178-6-1997 18Urban Traffic Arterials and the Sale Price of Single 19Urban Traffic Arterials and the Sale Price of Single 20Family Houses in Portland: A Hedonic Analysis of 21Family Houses in Portland: A Hedonic Analysis of 22Impacts 23Impacts 24Philip Wuest 25Portland State University 26Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds 27 Part of the Urban Studies and Planning Commons 28Let us know how access to this document benefits you. 29Recommended Citation 30Recommended Citation 31Wuest, Philip, "Urban Traffic Arterials and the Sale Price of Single Family Houses in Portland: A Hedonic 32Analysis of Impacts" (1997). Dissertations and Theses. Paper 6201. 33https://doi.org/10.15760/etd.8061 34This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and 35Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more 36accessible: pdxscholar@pdx.edu. 373839===== PAGE 28 (score 20, numeric-paren 0, table-words 2, var-words 5) =====4041Table 2: Descriptive Statistics 42Name 43Variable 44N 45Mean 46St.Dev. 47Minimum 48Maximum 49LN PRC 50Y2 511437 5211.33 530.44 5410.31 5512.89 56BATHRMS 57X1 581437 591.25 600.51 610.00 625.00 63FIREPLC 64X3 651437 660.79 670.66 680.00 693.00 70GARAGE 71X4 721437 731.00 740.74 750.00 765.00 77SQFTBLD 78XS 791437 801292.00 81493.48 82400.00 834032.00 84SQFTBD2 85X6 861437 871912700.00 881691400.00 89160000.00 9016257000.00 91SQFTLOT 92X7 931437 946767.00 953451.80 961850.00 9731800.00 98SQFTLOT2 99XS 1001437 10157699000.00 10280476000.00 1033422500.00 1041011200000.00 105STORIES 106X9 1071437 1081.10 1090.30 1100.00 1112.00 112VIEW 113X10 1141437 1150.07 1160.53 1170.00 1186.00 119STRUCAGE 120X11 1211437 12255.15 12324.10 1240.00 125134.00 126STRUCAG2 127X12 1281437 1293621.50 1302545.80 1310.00 13217956.00 133NEGHAGE 134XX4 1351437 13610.43 1379.53 1380.34 13965.91 140VANDAL 141XX6 1421437 14316.03 1446.83 1452.00 14665.00 147BURGLAR 148xxs 1491437 1503.42 1511.45 1521.00 1538.00 154MDFAMINC 155XX10 1561437 15731800.00 1589547.80 15914625.00 16082920.00 161NONWHTE 162XX11 1631437 16416.99 16515.04 1660.00 16778.06 168PCTBADEG 169XX12 1701437 17113.62 1728.54 1732.29 17444.99 175HIGH85 176V12 1771437 1780.02 1790.15 1800.00 1811.00 182HIGH350 183V13 1841437 1850.20 1860.40 1870.00 1881.00 189HIGH550 190V14 1911437 1920.15 1930.35 1940.00 1951.00 196Figure 1: Frequency Distribution: sale price and ln(sale price) 197HIST0'.3RAM - Sale Price 198PCT. 199N 2000.602 201865 202IXXXXXXXXXX 2030.577 204829 205IXXXXXXXXXX 2060.552 207793 208IXXXXXXXXXX 2090.527 210757 211IXXXXXXXXXX 2120.502 213721 214IXXXXXXXXXX 2150.477 216685 217IXXXXXXXXXX 2180.452 219649 220IXXXXXXXXXX 2210 427 222613 223IXXXXXXXXXX 2240.402 225577 226IXXXXXXXXXX 2270.376 228541 229IXXXXXXXXXX 230G.351 231505 232IXXXXXXXXXX 2330.326 234469 235IXXXXXXXXXX 2360.301 237433 238IXXXXXXXXXX 2390.276 240397 241IXXXXXXXXXXXXXXX 2420.251 243361 244IXXXXXXXXXXXXXXX 2450.226 246325 247IXXXXXXXXXXXXXXX 2480 201 249289 250IXXXXXXXXXXXXXXX 2510.176 252253 253IXXXXXXXXXXXXXXX 2540.151 255217 256IXXXXXXXXXXXXXXX 2570.126 258181 259IXXXXXXXXXXXXXXX 2600.101 261145 262IXXXXXXXXXXXXXXX 2630.076 264109 265IXXXXXXXXXXXXXXX 2660.051 26773 268I 2690.026 27037 271I 2720.001 2731 274I 275I---------I---------I---------I---------I---------1---------I 2760.300E+05 0.908E+05 0.152E+06 0.213E+06 0.273E+06 0.334E+06 0.395E+06 27719 278279280===== PAGE 29 (score 3, numeric-paren 0, table-words 1, var-words 1) =====281282HIS'I'CGRA.M -ln(sale price) 283PCT, 284N 2850,351 286505 2870,337 288484 2890,322 290463 2910,308 292442 2930,293 294421 2950,278 296400 2970,264 298379 2990.249 300358 3010,235 302337 3030 220 304316 3050,205 306295 3070,191 308274 3090,176 310253 3110,161 312232 3130,147 314211 3150,132 316190 3170,118 318169 3190,103 320148 3210,088 322127 3230,074 324106 3250,059 32685 3270,045 32864 3290,030 33043 3310,015 33222 3330,001 3341 335I 336I 337I 338I 339I 340I 341I 342I 343I 344I 345I 346I 347I 348I 349I 350I 351I 352I 353I 354I 355I 356I 357I 358I 359I 360xxxxxxxxxx 361xxxxxxxxxx 362xxxxxxxxxx 363xxxxxxxxxx 364xxxxxxxxxx 365xxxxxxxxxx 366xxxxxxxxxx 367xxxxxxxxxxxxxxx 368xxxxxxxxxxxxxxx 369xxxxxxx:xxxxxxx 370I---------I---------1---------I---------I---------I---------l 37110,3 37210,7 37311,2 37411,6 37512,0 37612,5 37712,9 378Development of the Arterial Variables: The Role of GIS 379Geographic information systems are a powerful tool in 380conducting spatial analysis of urban structure, attributes and systems. 381This section will describe the process that developed several of the 382components of the variables used in this study. 383The beginning data set for this analysis contained 13,589 384records; a complete record of all sales of single family homes in the 385City of Portland for 1994 (Figure 2). Each sale record was connected 386to both parcel attributes from the Multnomah County tax assessor and 387were further enhanced with neighborhood and structure attributes from 38820 389390391===== PAGE 46 (score 32, numeric-paren 0, table-words 6, var-words 5) =====392393w 394---.J 395Table 5: C 396ftest model 397Dependent variable: natural log of sale price 398Model 1: Adj. R"2 = .595 399Var. 400Name 401Coefficients 402t-score 403x1 404BATHRMS 4050.0439 4061.705 407x3 408FIREPLC 4090.0775 4105.38 411x4 412GARAGE 4130.0379 4142.638 415x5 416SQFTBLD 4170.0006 4188.467 419x6 420SQFTBD2 421-0.0000001 422-4.341 423x7 424SQFTLOT 4250.00001 4261.23 427x8 428SQFTLOT2 429-1E-11 430-0.325 431x9 432STORIES 4330.0512 4341.497 435x10 436VIEW 4370.0556 4384.39 439x11 440STRUCAGE 441-0.0037 442-2.56 443x12 444STRUCAG2 4450.00002 4461.57 447xx4 448NEGHAGE 449-0.0041 450-2.931 451xx6 452VANDAL 4530.0042 4542.49 455xx8 456BURGLAR 457-0.0304 458-4.117 459xx10 MDFAMINC 460-0.0000009 -0.3963 461xx11 462NONWHTE 463-0.0027 464-2.766 465xx12 PCTBADEG 466-0.0161 4677.064 468v1 469LINKDIST 470v5 471UNKNDUM 472-0.0107 473-0.477 474v6 475NOTRADUM 4760.0272 1.1.6 477v7 478WIN85 479v8 480WIN350 481v9 482WIN550 483Constant 48410.579 485Model 2: Adj. R"2 = .597 486Coefficients 487t-score 4880.0438 4891.702 4900.0781 4915.495 4920.0387 4932.72 4940.0006 4958.541 496-0.0000001 497-4.398 4980.000001 4991.047 500-4E-11 501-0.144 5020.0515 5031.506 5040.057 5054.537 506-0.0036 507-2.512 5080.00002 5091.547 510-0.004 511-2.831 5120.0041 5132.459 514-0.0297 515-4.117 516-0.000001 517-0.504 518-0.0029 519-3.007 5200.0164 5217.186 5220.00008 5233.083 52410.535 525Model 3: Adj. R"2 = .597 526Model 4: Adj. R"2 = .597 527Coefficients 528t-score Coefficients 529t-score 5300.0422 5311.635 5320.0422 5331.637 5340.0784 5355.494 5360.0786 5375.47 5380.0392 5392.758 5400.0393 5412.76 5420.00061 5438.512 5440.0006 5458.491 546-0.0000001 547-4.386 548-0.0000001 549-4.78 5500.000007 5510.947 5520.000007 5530.948 554-1 E-11 555-0.034 556-1 E-11 557-0.034 5580.0528 5591.538 5600.0533 5611.55 5620.0573 5634.654 5640.0575 5654.715 566-0.0036 567-2.485 568-0.0036 569-2.47 5700.00002 5711.522 5720.00002 5731.52 574-0.004 575-2.819 576-0.004 577-2.83 5780.0042 5792.436 5800.0042 5812.465 582-0.0295 583-4.112 584-0.03 585-4.036 586-0.000001 587-0.497 588-0.000001 589-0.495 590-0.003 591-3.04 592-0.003 593-3.007 5940.0165 5957.214 596-0.0165 5977.229 5980.00093 599-0.042 600-0.0082 601-0.38 602-0.0883 603-2.36 604-0.094 605-2.39 606-0.0603 607-3.265 608-0.066 609-2.944 610-0.0153 611-0.8 612-0.021 613-0.91 61410.605 61510.609 616617618===== PAGE 47 (score 24, numeric-paren 0, table-words 6, var-words 5) =====619620Equation 1: Final Model Specification 621Log( sale price) = 622/(x1 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 xx4 xx6 xx8 xx10 xx11 xx12 v12 v13 623v14) 624The results are shown in Table 6. 625Table 6: Model Results 626Variable 627Estimated 628Name 629Coefficient 630T-Score 631BATHRMS X1 6324.07E-02 6331.557 634FIREPLC X3 6357.60E-02 6365.283 637GARAGE X4 6383.90E-02 6392.733 640SQFTBLD XS 6416.00E-04 6428.346 643SQFTBD2 X6 644-1.00E-07 645-4.214 646SQFTLOT X7 6478.00E-06 6481.08 649SQFTLOT2 xa 650--6.00E-11 651-0.232 652STORIES X9 6535.30E-02 6541.547 655VIEW X10 6565.80E-02 6574.781 658STRUCAGE X11 659-3.S0E-03 660-2.466 661STRUCAG2 X12 6622.00E-05 6631.48 664NEGHAGE XX4 665-4.20E-03 666-3.001 667VANDAL XX6 6684.40E-03 6692.58 670BURGLAR xxa 671-3.26E-02 672-4.56 673MDFAMINC XX1C 674-1.00E-06 675-0.5 676NONWHTE XX1" 677-2.82E-03 678-2.868 679PCTBADEG XX1~ 6801.63E-02 6817.151 682HIGH85 V12 683-0.1022 684-2.331 685HIGH350 V13 686'-4.63E-02 687-2.237 688HIGH550 V14 6895.58E-03 6900.26 691CONSTANT 69210.54 693101 694* 695.4 696See footnote 697Coefficient 698P-Value 699lnterpretatlon* 7000.12 7014.070% 7020 7037.600% 7040.006 7053.900% 7060 7070.060% 7080 7090.000% 7100.28 7110.001% 7120.817 7130.000% 7140.122 7155.300% 7160 7175.800% 7180.014 719-0.360% 7200.139 7210.002% 7220.003 723-0.420% 7240.01 7250.440% 7260 727-3.260% 7280.618 7290.000% 7300.004 731-0.282% 7320 7331.630% 7340.02 735-9.802% 7360.025 737-4.545% 7380.794 7390.537% 7404The model specified is the semi-log form so that the coefficients can be interpreted to be 741the percent change in price for each unit of change in the independent variable. Kennedy 742(1981) gives the equation for interpreting the coefficients on dummy variables in the semi-743log form as g *:::::: ex~ c-744V(c))-1, where g* is the true effect of the dummy 745independent variable on the dependent variable and V(c), is the variance of the estimated 74638 747748749===== PAGE 48 (score 18, numeric-paren 0, table-words 9, var-words 0) =====750751The above model was estimated using a heteroskedasticity-752consistent covariance matrix. Earlier diagnostics on the model 753indicated non constant error variance across the sample, so the 754"hetcov" correction procedure (White 1993, pp. 79) was used with 755ordinary least squares regression to correct the error variance and 756improve confidence in both the coefficients and the t-scores. Table 6 757gives the model statistics: 758Table 7: Model Statistics 759F 760R2 761Adj. R2 762ow 763SSE 764107.059 765.60 766.5963 7671.9076 768112.96 769Table 8 illustrates a simulation of the results; the marginal effect of 770each variable on a $135,000 house. 771coefficient. These values will be calculated for the coefficients of the dummy variables 772Table 4, Coefficient Interpretation column. 77339 774775776===== PAGE 49 (score 11, numeric-paren 0, table-words 2, var-words 5) =====777778Table 8: Model Simulation 779Variable 780Coefficient 781Impact on 782Name 783Interpretation $135,000 784BATHRMS X1 7854.0700% 786$5,494.5 787FIREPLC X3 7887.6000% $10,260.0 789GARAGE X4 7903.9000% 791$5,265.0 792SQFTBLD XS 7930.0600% 794$81.0 795SQFTBD2 X6 7960.0000% 797$0.0 798SQFTLOT X7 7990.0008% 800$1.1 801SQFTLOT2 X8 8020.0000% 803$0.0 804STORIES X9 8055.3000% 806$7,155.0 807VIEW X10 8085.8000% 809$7,830.0 810STRUCAGE X11 811-0.3600% 812-$486.0 813STRUCAG2 X12 8140.0020% 815$2.7 816NEGHAGE XX4 817-0.4200% 818-$567.0 819VANDAL XX6 8200.4400% 821$594.0 822BURGLAR XX8 823-3.2600% -$4,401.0 824MDFAMINC XX1C 825-0.0001% 826-$0.1 827NONWHTE XX11 828-0.2820% 829-$380.7 830PCTBADEG XX1~ 8311.6300% 832$2,200.5 833HIGH85 V12 834-9.8019% -$13,232.6 835HIGH350 V13 836-4.5449% -$6,135.6 837HIGH550 V14 8380.5366% 839$724.4 84040 841842843===== PAGE 50 (score 15, numeric-paren 0, table-words 4, var-words 7) =====844845Part Five: Findings 846The results of the model are surprising in a couple of cases. The 847coefficient for stories is while only significantly different from zero at the 848.12 confidence level, is positive with a value of 5.3%. It was expected 849that with the size of structure controlled for that additional stories would 850lead to a reduction in price. Perhaps more surprising is the size of the 851coefficient for lot size, which shows an extremely small positive effect 852on price, although even this slight effect does not prove to be 853significantly different from zero. The impact of an additional bathroom 854on the sale price of a house is consistent with the work of Dubin and 855Sung (1990) who report about a 5% coefficient; Salvon (1996) finds 856the same effect to be, on average, 3% of sale price. In the current 857model, an additional bathroom is estimated to impact sale price 858positively at the margin by 4%. it is possible that another variable is 859picking up the effect. The number of fireplaces had the expected 860effect; the existence of one or more fire places in a house is often a 861proxy for housing quality or amenity level. The sign on building square 862feet met expectations, and the result of a negative coefficient on the 863squared value of building square feet is consistent with the theory of 86441 865