1# RECORD 0aed4606387835599541d79050eabb05449e04422# TITLE: The effect of Movida on residential property prices: up or down?3# YEAR: 2023.0 VENUE: Applied Economics DOI: 10.1080/00036846.2022.21590084# AUTHORS: E. Ottoz; P. Pavese; L. Sella5# PAGES: 36 CANDIDATE TABLE PAGES (1-based): [33, 34, 35]6# ABSTRACT: ABSTRACT Hedonic regression technique has often been used to study the effect of road, railway and airport noise on property prices. However, European cities are experiencing a particular type of noise pollution originated by night-time recreational activities mainly located in the city centres, the so-called ‘Movida’, which has not been properly investigated yet. The aim of this paper is to examine the effect of recreational noise on residential property prices. We used an original highly detailed housing transactions dataset from the City of Turin covering the period 2017 to 2018 and built an indicator of recreational noise based on the proximity of dwellings to the night recreational activities. The results obtained employing hedonic modelling show that the adverse environment for an apartment located in a ‘Movida’ district will result in a lower market value as compared to an apartment with similar characteristics, except for recreational noise. This occurs because potential buyers reduce their demand, as they discount the present value of the costs of annoyance, loss of tranquillity and health effects due to sleep deprivation.789===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) =====101120 October 202612AperTO - Archivio Istituzionale Open Access dell'Università di Torino13Original Citation:14The effect of Movida on residential property prices: up or down?15Published version:16DOI:10.1080/00036846.2022.215900817Terms of use:18Open Access19(Article begins on next page)20Anyone can freely access the full text of works made available as "Open Access". Works made available21under a Creative Commons license can be used according to the terms and conditions of said license. Use22of all other works requires consent of the right holder (author or publisher) if not exempted from copyright23protection by the applicable law.24Availability:25This is the author's manuscript26This version is available http://hdl.handle.net/2318/188500027since282023-01-08T08:41:09Z293031===== PAGE 33 (score 25, numeric-paren 0, table-words 3, var-words 11) =====3233Table 0. Hedonic pricing empirical literature including environmental noise. 34 35 36 37 38Table 1. Standardized number of transactions (%) by dimensional classes in the OMI sample, 39central subsample and in the whole city of Turin (population). 40 41 42<50 sqm 4351-85 sqm 4486-115 sqm 45116-145 sqm 46>146 sqm 47Population, whole city 4812.9 4946.6 5023.7 519.2 527.7 53OMI sample, whole city 548.9 5536.8 5629.0 5712.2 5813.1 59Central Subsample 608.2 6132.3 6230.2 6314.5 6414.8 65 66Data source: Real Estate Market Overview (Pwc, 2018) and our elaborations 67 68 69Table 2. Descriptive statistics in the central and suburb districts. 70 71 72Full sample (N = 6,861) 73Central subsample** (N = 1,788) 74Variable 75Mean 76SD 77Min 78Max 79Mean 80SD 81Min 82Max 83Price 84214,331 85184,292 10,000 862,750,000 229,641 87196,651 8810,000 892,200,000 90Total surface 9199.24 9248.18 9320 94422 95104.18 9652.92 9720 98650 99Age* 10041.76 10122.80 1026 103219 10440.47 10524.64 1066 107219 108Floor level 1093.04 1101.90 1111 1129 1133.03 1141.88 1151 1169 117Number of baths 1181.23 1190.50 1201 1214 1221.27 1230.54 1241 1255 126Economic building (d) 1270.39 1280.49 1290 1301 1310.27 1320.44 1330 1341 135Residential building (d) 1360.51 1370.50 1380 1391 1400.62 1410.48 1420 1431 144Historical building (d) 1450.10 1460.30 1470 1481 1490.10 1500.30 1510 1521 153Green (<500m, d) 1540.54 1550.50 1560 1571 1580.54 1590.50 1600 1611 162Transport stop (<500m, d) 1630.99 1640.10 1650 1661 1670.99 1680.09 1690 1701 171Distance train station (>1km, d) 1720.94 1730.24 1740 1751 1760.92 1770.27 1780 1791 180Car park 1810.05 1820.22 1830 1841 1850.06 1860.23 1870 1881 189Commercial 1900.99 1910.09 1920 1931 1940.99 1950.09 1960 1971 198View of river or mountains 1990.16 2000.37 2010 2021 2030.19 2040.39 2050 2061 207Time-on-the-market (>1y, d) 2080.73 2090.44 2100 2111 2120.66 2130.47 2140 2151 216Month-year (d) 217Yes 218Yes 219Yes 220Yes 221Yes 222Yes 223Yes 224Yes 225Urban macro-area (d) 226Yes 227Yes 228Yes 229Yes 230Yes 231Yes 232Yes 233Yes 234Dummy movida 50 2350.13 2360.34 2370 2381 2390.14 2400.35 2410 2421 243Dummy movida 75 2440.22 2450.41 2460 2471 2480.23 2490.42 2500 2511 252Dummy movida 100 2530.32 2540.47 2550 2561 2570.33 2580.47 2590 2601 261Movida 50 (#) 2620.20 2630.62 2640 2658 2660.21 2670.61 2680 2696 270Movida 51-75 (#) 2710.20 2720.60 2730 2747 2750.20 2760.56 2770 2786 279Movida 76-100 (#) 2800.29 2810.80 2820 2839 2840.29 2850.78 2860 2877 288* Age is defined as the difference between the date on which the residential property was sold and the date of its eventual renovation 289** Central districts are identified by OMI areas B1 – B9 and C3 290 291292293===== PAGE 34 (score 152, numeric-paren 110, table-words 8, var-words 10) =====294295Table 3. Regression results for the full sample (columns 1-2) and the central subsample (columns 3-2964). Model: log-log fully interacted specification; two-way clustered standard errors in parentheses. 297Dependent variable: log(price). Columns A and B: Wald test for the equality of coefficients with 298respect to time-on-the-market (< 1 year, ≥ 1 year) for each covariate. 299 300 301Full sample 302Central subsample 303VARIABLES 304(1) 305(2) 306(A) 307(3) 308(4) 309(B) 310 311< 1 year on 312the market 313≥ 1 year on 314the market 315T-test 316pvalue 317< 1 year on 318the market 319≥ 1 year on 320the market 321T-test 322pvalue 323 324 325 326Green 3270.006 3280.075*** 3290.000 3300.021 3310.085*** 3320.021 333(<500m) 334(0.014) 335(0.007) 336 337(0.023) 338(0.014) 339 340Transport stop 3410.245*** 3420.110*** 3430.026 344-1.028*** 3450.050 3460.000 347(<500m) 348(0.041) 349(0.041) 350 351(0.233) 352(0.079) 353 354Distance train station 3550.010 356-0.093*** 3570.000 3580.020 359-0.155*** 3600.000 361(>1km) 362(0.020) 363(0.019) 364 365(0.024) 366(0.037) 367 368Car park 3690.077*** 3700.186*** 3710.003 3720.135*** 3730.159*** 3740.691 375(0.027) 376(0.026) 377 378(0.048) 379(0.044) 380 381Commercial 3820.344*** 3830.374*** 3840.740 3850.300*** 3860.406*** 3870.116 388(0.037) 389(0.084) 390 391(0.057) 392(0.028) 393 394View river/mountains 3950.144*** 3960.131*** 3970.600 3980.117*** 3990.188*** 4000.091 401(0.021) 402(0.012) 403 404(0.031) 405(0.027) 406 407Number of baths+ 4080.449*** 4090.110*** 4100.000 4110.386*** 4120.060** 4130.001 414(0.036) 415(0.016) 416 417(0.069) 418(0.028) 419 420Economic building 421-0.277*** 422-0.327*** 4230.116 424-0.364*** 425-0.389*** 4260.642 427(0.028) 428(0.009) 429 430(0.051) 431(0.016) 432 433Historical building 4340.350*** 4350.310*** 4360.174 4370.356*** 4380.231*** 4390.003 440(0.023) 441(0.021) 442 443(0.032) 444(0.031) 445 446Age+ 447-0.047*** 448-0.016** 4490.076 450-0.064*** 4510.010 4520.009 453(0.015) 454(0.008) 455 456(0.020) 457(0.017) 458 459Total surface+ 4601.028*** 4610.961*** 4620.029 4631.093*** 4640.948*** 4650.001 466(0.029) 467(0.014) 468 469(0.040) 470(0.026) 471 472Floor level+ 473-0.011 4740.020*** 4750.005 4760.001 4770.024** 4780.190 479(0.010) 480(0.005) 481 482(0.014) 483(0.011) 484 485Movida 50 (#) 486-0.008 4870.000 4880.552 4890.021 4900.008 4910.599 492(0.012) 493(0.006) 494 495(0.023) 496(0.013) 497 498Movida_51-75 (#) 4990.008 500-0.002 5010.573 502-0.039* 503-0.025* 5040.514 505(0.019) 506(0.005) 507 508(0.021) 509(0.013) 510 511Movida 76-100 (#) 512-0.000 5130.002 5140.884 5150.006 5160.008 5170.904 518(0.013) 519(0.005) 520 521(0.011) 522(0.011) 523 524Time (d) 525Yes 526Yes 527- 528Yes 529Yes 530- 531Urban macro-area (d) 532Yes 533Yes 534- 535Yes 536Yes 537- 538Constant 5396.533*** 5406.533*** 541- 5427.556*** 5437.556*** 544- 545(0.147) 546(0.147) 547 548(0.153) 549(0.153) 550 551Observations 5526,861 553 5541,788 555 556R-squared 5570.865 558 5590.873 560 561+ Variables in logarithmic form 562*** p<0.01, ** p<0.05, * p<0.1 563 564 565Table 4. Quantile regression results on the central subsample, dwellings on the market for more 566than one year (N=1,176). Model: log-log specification. Dependent variable: log(price). 567 568569570===== PAGE 35 (score 280, numeric-paren 240, table-words 1, var-words 9) =====571572 573(1) 574(2) 575(3) 576(4) 577(5) 578(6) 579(7) 580(8) 581(9) 582Variables 583q10 584q20 585q30 586q40 587q50 588q60 589q70 590q80 591q90 592 593 594 595 596 597 598 599 600 601 602Green 6030.108*** 6040.118*** 6050.116*** 6060.105*** 6070.115*** 6080.099*** 6090.086*** 6100.044* 6110.055* 612(<500m) 613(0.027) 614(0.023) 615(0.024) 616(0.021) 617(0.021) 618(0.021) 619(0.021) 620(0.025) 621(0.030) 622Transport 6230.381* 6240.103 6250.109 626-0.080 627-0.057 628-0.037 629-0.120 630-0.001 631-0.025 632 (<500m) 633(0.203) 634(0.167) 635(0.160) 636(0.150) 637(0.125) 638(0.106) 639(0.098) 640(0.090) 641(0.102) 642Trainstation -0.211*** 643-0.168*** -0.128*** -0.134*** -0.124*** -0.126*** 644-0.111* 645-0.144** 646-0.158** 647(>1km) 648(0.062) 649(0.056) 650(0.049) 651(0.045) 652(0.046) 653(0.049) 654(0.059) 655(0.060) 656(0.080) 657Car park 658-0.134 6590.045 6600.206** 6610.181*** 6620.221*** 6630.207*** 6640.206*** 6650.216*** 6660.218*** 667 668(0.132) 669(0.142) 670(0.097) 671(0.067) 672(0.056) 673(0.045) 674(0.046) 675(0.056) 676(0.064) 677Commercial 6780.066 6790.111* 6800.197*** 6810.274*** 6820.465*** 6830.529*** 6840.593*** 6850.667*** 6860.773*** 687 688(0.075) 689(0.063) 690(0.070) 691(0.077) 692(0.073) 693(0.058) 694(0.052) 695(0.062) 696(0.093) 697View river/ 6980.195*** 6990.220*** 7000.215*** 7010.219*** 7020.229*** 7030.195*** 7040.172*** 7050.142*** 7060.149*** 707mountains 708(0.054) 709(0.042) 710(0.036) 711(0.035) 712(0.037) 713(0.038) 714(0.039) 715(0.045) 716(0.045) 717Number 7180.036 7190.041 7200.043 7210.075* 7220.120*** 7230.101*** 7240.095** 7250.092** 7260.106** 727of baths+ 728(0.055) 729(0.050) 730(0.044) 731(0.040) 732(0.038) 733(0.037) 734(0.041) 735(0.045) 736(0.052) 737Economic 738-0.381*** 739-0.361*** -0.371*** -0.369*** -0.392*** -0.402*** -0.415*** -0.422*** -0.376*** 740Building 741(0.032) 742(0.025) 743(0.027) 744(0.025) 745(0.025) 746(0.026) 747(0.026) 748(0.029) 749(0.038) 750Historical 7510.184*** 7520.253*** 7530.284*** 7540.275*** 7550.245*** 7560.235*** 7570.210*** 7580.173*** 7590.142** 760Building 761(0.070) 762(0.071) 763(0.057) 764(0.047) 765(0.044) 766(0.040) 767(0.039) 768(0.044) 769(0.058) 770Age+ 771-0.006 772-0.012 7730.017 7740.034 7750.048** 7760.034 7770.031 778-0.002 779-0.008 780 781(0.033) 782(0.025) 783(0.022) 784(0.022) 785(0.024) 786(0.023) 787(0.021) 788(0.026) 789(0.028) 790Total 7911.002*** 7921.000*** 7930.950*** 7940.933*** 7950.904*** 7960.911*** 7970.917*** 7980.917*** 7990.899*** 800Surface+ 801(0.052) 802(0.043) 803(0.041) 804(0.037) 805(0.033) 806(0.030) 807(0.034) 808(0.033) 809(0.035) 810Floor level 8110.033* 8120.028* 8130.038** 8140.034** 8150.030* 8160.020 8170.017 8180.015 8190.029 820 821(0.019) 822(0.015) 823(0.017) 824(0.016) 825(0.016) 826(0.015) 827(0.016) 828(0.017) 829(0.022) 830Movida 8310.025 8320.016 8330.006 8340.019 8350.022 8360.020 8370.016 8380.023 839-0.002 84050 (#) 841(0.024) 842(0.017) 843(0.019) 844(0.018) 845(0.017) 846(0.016) 847(0.015) 848(0.017) 849(0.020) 850Movida 8510.000 852-0.031 853-0.026 854-0.029* 855-0.032** 856-0.028* 857-0.038** 858-0.039** 859-0.032 860 51_75 (#) 861(0.021) 862(0.019) 863(0.020) 864(0.016) 865(0.014) 866(0.014) 867(0.016) 868(0.020) 869(0.025) 870Movida 871-0.027 8720.000 8730.000 8740.010 8750.014 8760.007 8770.017 878-0.001 8790.017 880 76_100 (#) 881(0.017) 882(0.018) 883(0.019) 884(0.014) 885(0.011) 886(0.011) 887(0.012) 888(0.015) 889(0.022) 890Urban area 891yes 892yes 893yes 894yes 895yes 896yes 897yes 898yes 899yes 900Time 901yes 902yes 903yes 904yes 905yes 906yes 907yes 908yes 909yes 910Constant 9117.278*** 9127.496*** 9137.604*** 9147.811*** 9157.696*** 9167.718*** 9177.787*** 9187.920*** 9197.973*** 920 921(0.343) 922(0.265) 923(0.257) 924(0.240) 925(0.215) 926(0.206) 927(0.216) 928(0.211) 929(0.229) 930R-squared 9310.612 9320.616 9330.624 9340.635 9350.643 9360.650 9370.654 9380.658 9390.661 940Standard errors in parentheses 941*** p<0.01, ** p<0.05, * p<0.1 942 943 944Figure 1. Effect of “Movida” variables (number of recreational activities in the specified range) on 945prices in the central subsample, different specifications. 946947948===== PAGE 36 (score 0, numeric-paren 0, table-words 0, var-words 0) =====949950 951 952