1# RECORD 2799534456858542797f23b6cf17bd1f124398192# TITLE: Towards a Hedonic Pricing Method for the Bucharest Private Housing Market3# YEAR: 2019.0 VENUE: Scientific Annals of Economics and Business DOI: 10.47743/saeb-2019-00314# AUTHORS: Ion Radu Zilișteanu; R. Muntean; Ș. Gherghina; G. Vintilă; T. Barbu5# PAGES: 25 CANDIDATE TABLE PAGES (1-based): [11, 14, 15, 16, 17, 18, 19, 20, 21]6# ABSTRACT: This paper aims at exploring the drivers of dwellings’sale prices in Bucharest, Romania, over the period 2014-2018. Several housing structural attributes are covered, such as the number of rooms, useful and constructed surface, type of comfort, floor, the number of bathrooms, balconies and parking, the seniority from construction, as well as from renovation, structure type, height regime, and the duration until completion of the real estate transaction. By estimating a standard hedonic price model via OLS regression for a sample of 765 transactions, we notice that all the selected variables, except the floor level and seniority from construction, positively influence the property prices. However, in case of useful and constructed surface, nonlinear relationships with property prices were acknowledged. Robustness checks in form of quantile regressions reinforce the empirical findings.789===== PAGE 1 (score 12, numeric-paren 0, table-words 0, var-words 12) =====1011 12 13 14Scientific Annals of Economics and Business 1566 (3), 2019, 389-413 16DOI: 10.2478/saeb-2019-0031 17 18 19Towards a Hedonic Pricing Method for the Bucharest Private Housing Market 20Ion Radu Zilișteanu*, Răzvan Muntean**, Ștefan Cristian Gherghina***, 21Georgeta Vintilă22§23*, Teodora Cristina Barbu°* 24 25 26Abstract 27This paper aims at exploring the drivers of dwellings’sale prices in Bucharest, Romania, over the period 282014-2018. Several housing structural attributes are covered, such as the number of rooms, useful and 29constructed surface, type of comfort, floor, the number of bathrooms, balconies and parking, the 30seniority from construction, as well as from renovation, structure type, height regime, and the duration 31until completion of the real estate transaction. By estimating a standard hedonic price model via OLS 32regression for a sample of 765 transactions, we notice that all the selected variables, except the floor 33level and seniority from construction, positively influence the property prices. However, in case of useful 34and constructed surface, nonlinear relationships with property prices were acknowledged. Robustness 35checks in form of quantile regressions reinforce the empirical findings. 36 37Keywords: hedonic regression; OLS; quantiles; structural attributes. 38 39JEL classification: C21; R30. 40 41 42 431. INTRODUCTION 44 45The pecuniary value of nonmarket goods and services may be estimated via multifarious 46ways such as the contingent valuation method, the travel cost method, or the hedonic pricing 47method (Dahal et al., 2019). Among them, the hedonic pricing method is widely used (Selim, 482009; Z. Z. Huang et al., 2017), having as the main supposition that a homebuyer does not only 49 50* Department of Finance, Faculty of Finance and Banking, The Bucharest University of Economic Studies, Romania; 51e-mail: rzilisteanu@gmail.com (corresponding author). 52** SC RUN IT SOLUTIONS SRL; e-mail: razvan@run.ro. 53*** Department of Finance, Faculty of Finance and Banking, The Bucharest University of Economic Studies, Romania; 54e-mail: stefan.gherghina@fin.ase.ro. 55§ Department of Finance, Faculty of Finance and Banking, The Bucharest University of Economic Studies, Romania; 56e-mail: vintilageorgeta@yahoo.fr. 57° Department of Money and Banking, Faculty of Finance and Banking, The Bucharest University of Economic 58Studies, Romania; e-mail: teodorabarbu65@yahoo.com. 596061===== PAGE 11 (score 20, numeric-paren 5, table-words 4, var-words 5) =====6263Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 389-413 64399 65 66Variables 67Value 68Count 69Cumulative - Count 70Percent 71Cumulative - Percent 721 73126 74765 7516.47059 76100 77Missing 780 79765 800 81100 82Height_regime_6 830 84725 85725 8694.77124 8794.7712 881 8940 90765 915.22876 92100 93Missing 940 95765 960 97100 98Height_regime_7 990 100736 101736 10296.20915 10396.2092 1041 10529 106765 1073.79085 108100 109Missing 1100 111765 1120 113100 114Height_regime_8 1150 116742 117742 11896.99346 11996.9935 1201 12123 122765 1233.00654 124100 125Missing 1260 127765 1280 129100 130Height_regime_9 1310 132630 133630 13482.35294 13582.3529 1361 137135 138765 13917.64706 140100 141Missing 1420 143765 1440 145100 146Height_regime_10 1470 148728 149728 15095.1634 15195.1634 1521 15337 154765 1554.8366 156100 157Missing 1580 159765 1600 161100 162Source: Authors’ computations 163 164The output related to independent samples t test towards the sale price and structural 165attributes is exhibited in Table no. 4. We notice that there is a significant difference in price 166between apartments that are comfort 1 and those comfort 2. Therewith, significant differences 167in price are registered between apartments showing height regime Ground Floor + 10 Floors 168and the rest of dwellings. Similar statements are valid for Ground Floor + 2 Floors, Ground 169Floor + 5 Floors and, Ground Floor + 6 Floors. 170Table no. 5 exposes the results of independent samples t test regarding the duration until 171transaction completion and structural attributes. Thus, there is a statistically significant 172difference between the mean duration of apartments comfort 1 and those comfort 2. In the 173same vein, significant differences in duration occur between dwellings with height regime 174Ground Floor + 10 Floorsand the rest of apartments. Analogous remarks are true for Ground 175Floor + 3 Floors and Ground Floor + 5 Floors. 176The correlations between variables are pointed out in Table no. 6. Thereby, sale price is 177highly correlated with the useful (0.73) and constructed surface of the apartment (0.74). 178Likewise, we notice high correlations between the number of rooms and useful surface 179(0.80), the number of rooms and the constructed surface (0.75), as well as between the useful 180surface and constructed area (0.97). As such, the previously mentioned variables will be 181employed in distinct regression equations in order to avoid the multicolinearity issue. 182 1834.2 The outcomes of hedonic pricing regressions 184 185The impact of each housing structural attributes on the sale price is reported in Table no. 1867. Thus, most of the housing structural characteristic exert a positive influence on the housing 187price. The comfort exhibits the highest positive influence on price (Eq. 4), but the lowest 188impact is registered in case of surface, both useful (Eq. 2) and constructed (Eq. 3). 189 190 191192193===== PAGE 12 (score 4, numeric-paren 0, table-words 2, var-words 0) =====194195400 196Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 197 198Table no. 4 – The outcomes of independent samples t test regarding the sale price and structural attributes (2014-2018) 199 Source: Authors’ computations 200 201Table no. 5 – The outcomes of independent samples t test regarding the duration until transaction completion and 202structural attributes (2014-2018) 203Source: Authors’ computations 204 205 206 207208209===== PAGE 14 (score 70, numeric-paren 49, table-words 1, var-words 11) =====210211402 212Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 213 214The number of rooms show a positive impact (Eq. 1) as in Selim (2009), Baranzini and 215Schaerer (2011). Likewise, if useful surface and constructed area increases by 1%, price will 216increase by 0.01%, the association being alike previous studies (Wilhelmsson, 2002; Keskin, 2172008; Jim and Chen, 2009; Selim, 2009; Chen and Jim, 2010; Conroy and Milosch, 2011; Hui 218et al., 2012; Efthymiou and Antoniou, 2013; Z. Z. Huang et al., 2017; Wu et al., 2017; Yang 219et al., 2019). In addition, the number of bathrooms positively influence sale price (Eq. 6) alike 220D Dahal et al. (2019), Mulley et al. (2016), Mulley et al. (2018), Tian et al. (2017), Conroy 221et al. (2013), Conroy and Milosch (2011), Larsen and Blair (2014), Ibeas et al. (2012), 222alongside the number of parking (Eq. 8) similar Mulley et al. (2018) and the number of 223balconies (Eq. 7). The effect of structure on price is positive (Eq. 11), opposite to Selim 224(2009). The floor level shows a negative impact on sale price (Eq. 5), contrary to Jim and 225Chen (2009), Chen and Jim (2010), Baranzini and Schaerer (2011), Hui et al. (2012), Ibeas 226et al. (2012), Efthymiou and Antoniou (2013), Wu et al. (2017) . The number of years since 227construction exhibit a negative impact on price (Eq. 9), as in earlier studies (Wilhelmsson, 2282002; Jim and Chen, 2009; Selim, 2009; Hui et al., 2012; Conroy et al., 2013; Czembrowski 229and Kronenberg, 2016; Tian et al., 2017; Wen et al., 2017b; Brecard et al., 2018; Lu, 2018; 230Laszkiewicz et al., 2019; Li et al., 2019; Yang et al., 2019). In case of the number of years 231since renovation, the impact is not statistically significant (Eq. 10), as opposed to Zhang and 232Leonard (2014). The highest goodness of fit is registered in case of Eq. 1 and Eq. 2, whereas 233the lowest explanatory power is depicted by Eq. 10 and Eq. 11. 234 235Table no. 7 – OLS regressions’ results regarding the impact of each housing structural attributes 236on the sale price (duration until transaction completion not included) 237Variables 238(1) 239(2) 240(3) 241(4) 242(5) 243(6) 244(7) 245(8) 246(9) 247(10) 248(11) 249No_rooms 2500.31*** 251 252 253 254 255 256 257 258 259 260 261 262(22.75) 263 264 265 266 267 268 269 270 271 272 273S_useful 274 2750.01*** 276 277 278 279 280 281 282 283 284 285 286 287(36.13) 288 289 290 291 292 293 294 295 296 297S_constructed 298 299 3000.01*** 301 302 303 304 305 306 307 308 309 310 311 312(35.20) 313 314 315 316 317 318 319 320 321Comfort 322 323 324 3250.52*** 326 327 328 329 330 331 332 333 334 335 336 337(11.05) 338 339 340 341 342 343 344 345Floor 346 347 348 349 350-0.02*** 351 352 353 354 355 356 357 358 359 360 361 362(-4.26) 363 364 365 366 367 368 369No_bathrooms 370 371 372 373 374 3750.49*** 376 377 378 379 380 381 382 383 384 385 386 387(17.23) 388 389 390 391 392 393No_balconies 394 395 396 397 398 399 4000.29*** 401 402 403 404 405 406 407 408 409 410 411 412(8.71) 413 414 415 416 417No_parking 418 419 420 421 422 423 424 4250.30*** 426 427 428 429 430 431 432 433 434 435 436 437(4.74) 438 439 440 441Seniority_construction 442 443 444 445 446 447 448 449 450-0.01*** 451 452 453 454 455 456 457 458 459 460 461 462(-7.60) 463 464 465Seniority_renovation 466 467 468 469 470 471 472 473 474 475-0.00 476 477 478 479 480 481 482 483 484 485 486 487(-1.23) 488 489Structure 490 491 492 493 494 495 496 497 498 499 5000.08† 501 502 503 504 505 506 507 508 509 510 511 512(1.65) 513_cons 51410.29*** 10.12*** 51510.17*** 10.50*** 11.11*** 10.40*** 10.70*** 10.79*** 11.21*** 11.00*** 10.92*** 516 517(304.83) (386.15) (399.40) (235.16) (393.12) (277.26) (259.13) (169.27) (357.95) (471.73) (230.38) 518F statistic 519517.48*** 1305.34*** 1239.18*** 122.17*** 18.16*** 296.74*** 75.84*** 22.44*** 57.76*** 5201.50 5212.72† 522R-sq 5230.40 5240.63 5250.62 5260.14 5270.02 5280.28 5290.10 5300.07 5310.08 5320.00 5330.00 534# 535765.00 536760.00 537762.00 538734.00 765.00 765.00 706.00 317.00 685.00 451.00 669.00 539Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 540541542===== PAGE 15 (score 102, numeric-paren 68, table-words 7, var-words 10) =====543544Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 389-413 545403 546 547The impact of every housing structural feature on the sale price, when controlling for the 548duration until transaction completion, is pointed out in Table no. 8. The results are matching 549with those displayed in Table no. 7. 550 551Table no. 8 – OLS regressions’ results regarding the impact of each housing structural attributes 552on the sale price(duration until transaction completion included) 553Variables 554(1) 555(2) 556(3) 557(4) 558(5) 559(6) 560(7) 561(8) 562(9) 563(10) 564(11) 565No_rooms 5660.30*** 567 568 569 570 571 572 573 574 575 576 577 578(22.31) 579 580 581 582 583 584 585 586 587 588 589S_useful 590 5910.01*** 592 593 594 595 596 597 598 599 600 601 602 603(35.48) 604 605 606 607 608 609 610 611 612 613S_constructed 614 615 6160.01*** 617 618 619 620 621 622 623 624 625 626 627 628(34.56) 629 630 631 632 633 634 635 636 637Comfort 638 639 640 6410.51*** 642 643 644 645 646 647 648 649 650 651 652 653(10.72) 654 655 656 657 658 659 660 661Floor 662 663 664 665 666-0.02*** 667 668 669 670 671 672 673 674 675 676 677 678(-4.23) 679 680 681 682 683 684 685No_bathrooms 686 687 688 689 690 6910.48*** 692 693 694 695 696 697 698 699 700 701 702 703(17.01) 704 705 706 707 708 709No_balconies 710 711 712 713 714 715 7160.28*** 717 718 719 720 721 722 723 724 725 726 727 728(8.47) 729 730 731 732 733No_parking 734 735 736 737 738 739 740 7410.28*** 742 743 744 745 746 747 748 749 750 751 752 753(4.39) 754 755 756 757Seniority_construction 758 759 760 761 762 763 764 765 766-0.01*** 767 768 769 770 771 772 773 774 775 776 777 778(-6.98) 779 780 781Seniority_renovation 782 783 784 785 786 787 788 789 790 791-0.00 792 793 794 795 796 797 798 799 800 801 802 803(-1.32) 804 805Structure 806 807 808 809 810 811 812 813 814 815 8160.10* 817 818 819 820 821 822 823 824 825 826 827 828(1.97) 829Duration 8300.00* 831-0.00 832-0.00 8330.00 8340.00*** 0.00*** 8350.00** 8360.00† 8370.00** 8380.00** 0.00*** 839 840(2.45) 841(-0.26) 842(-0.79) 843(1.56) 844(4.19) 845(3.55) 846(2.73) 847(1.67) 848(3.05) 849(3.05) 850(4.20) 851_cons 85210.27*** 10.12*** 10.17*** 10.50*** 11.06*** 10.37*** 10.68*** 10.77*** 11.16*** 10.95*** 10.86*** 853 854(301.99) (384.10) (397.60) (234.22) (362.92) (273.54) (255.30) (167.85) (313.43) (401.74) (220.55) 855F statistic 856263.46*** 651.90*** 619.59*** 62.43*** 18.05*** 156.90*** 41.99*** 12.68*** 33.89*** 5.40** 10.24*** 857R-sq 8580.41 8590.63 8600.62 8610.15 8620.05 8630.29 8640.11 8650.07 8660.09 8670.02 8680.03 869# 870765.00 871760.00 872762.00 734.00 765.00 765.00 706.00 317.00 685.00 451.00 669.00 873Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 874 875Table no. 9 and Table no. 10 exposes the effect of each height regime on the sale price. 876We notice mixed relationships, namely negative in case of the Ground Floor + 10 Floors (Eq. 8771), but positive in case of Ground Floor + 11 Floors (Eq. 2), Ground Floor + 2 Floors (Eq. 8783), Ground Floor + 5 Floors (Eq. 6), Ground Floor + 6 Floors (Eq. 7), Ground Floor + 8 Floors 879(Eq. 9). As well, the regression coefficients do not differ when controlling for duration until 880transaction completion). 881Table no. 11 and Table no. 12 exhibits the impact of the entire range of housing structural 882attributes on the sale price. Similar to previous estimations, the positive impact related to the 883number of rooms (Eq. 1 and Eq. 2), comfort (Eq. 1 – Eq. 10), number of bathrooms (Eq. 1, Eq. 8847 and, Eq. 8) and parking (Eq. 1, Eq. 2, Eq. 4 – Eq. 6, Eq. 8 and, Eq. 10), alongside structure 885(Eq. 3, Eq. 7, Eq. 9 and, Eq. 10), maintains its statistical significance. Therewith, the negative 886influence of the floor level (Eq. 1, Eq. 4, Eq. 6, Eq. 8 and, Eq. 10) and seniority from construction 887(Eq. 1, Eq. 2, Eq. 6 and, Eq. 10) is strengthened. However, the effect of the number of balconies 888(Eq. 7 and Eq. 9) and seniority from renovation (Eq. 4) turn out to be negative, but the statistical 889significance is week. 890891892===== PAGE 16 (score 126, numeric-paren 106, table-words 2, var-words 0) =====893894404 895Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 896 897Table no. 9 – OLS regressions’ outcomes towards the influence of each height regime on the sale 898price (duration until transaction completion not covered) 899Variables 900(1) 901(2) 902(3) 903(4) 904(5) 905(6) 906(7) 907(8) 908(9) 909(10) 910Height_regime_1 911-0.23*** 912 913 914 915 916 917 918 919 920 921 922(-7.75) 923 924 925 926 927 928 929 930 931 932Height_regime_2 933 9340.18* 935 936 937 938 939 940 941 942 943 944 945(2.06) 946 947 948 949 950 951 952 953 954Height_regime_3 955 956 9570.30** 958 959 960 961 962 963 964 965 966 967 968(3.09) 969 970 971 972 973 974 975 976Height_regime_4 977 978 979 980-0.01 981 982 983 984 985 986 987 988 989 990 991(-0.23) 992 993 994 995 996 997 998Height_regime_5 999 1000 1001 1002 10030.04 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014(1.04) 1015 1016 1017 1018 1019 1020Height_regime_6 1021 1022 1023 1024 1025 10260.23*** 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037(3.37) 1038 1039 1040 1041 1042Height_regime_7 1043 1044 1045 1046 1047 1048 10490.17* 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060(2.19) 1061 1062 1063 1064Height_regime_8 1065 1066 1067 1068 1069 1070 1071 10720.06 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083(0.71) 1084 1085 1086Height_regime_9 1087 1088 1089 1090 1091 1092 1093 1094 10950.13*** 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106(3.38) 1107 1108Height_regime_10 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118-0.05 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129(-0.66) 1130_cons 113111.09*** 11.00*** 11.00*** 11.01*** 11.00*** 10.99*** 11.00*** 11.00*** 10.98*** 11.01*** 1132 1133(617.04) (725.05) (729.38) (704.72) (671.37) (719.60) (722.30) (723.52) (670.13) (716.89) 1134F statistic 113560.04*** 11364.24* 11379.54** 11380.05 11391.08 114011.35*** 11414.79* 11420.50 114311.45*** 11440.43 1145R-sq 11460.07 11470.01 11480.01 11490.00 11500.00 11510.01 11520.01 11530.00 11540.01 11550.00 1156# 1157765.00 1158765.00 1159765.00 1160765.00 1161765.00 1162765.00 1163765.00 1164765.00 1165765.00 1166765.00 1167Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 1168 1169Table no. 10 – OLS regressions’ outcomes towards the influence of each height regime on the 1170sale price (duration until transaction completion covered) 1171Variables 1172(1) 1173(2) 1174(3) 1175(4) 1176(5) 1177(6) 1178(7) 1179(8) 1180(9) 1181(10) 1182Height_regime_1 1183-0.22*** 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194(-7.35) 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204Height_regime_2 1205 12060.18* 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217(2.04) 1218 1219 1220 1221 1222 1223 1224 1225 1226Height_regime_3 1227 1228 12290.27** 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240(2.87) 1241 1242 1243 1244 1245 1246 1247 1248Height_regime_4 1249 1250 1251 1252-0.05 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263(-0.99) 1264 1265 1266 1267 1268 1269 1270Height_regime_5 1271 1272 1273 1274 12750.04 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286(1.09) 1287 1288 1289 1290 1291 1292Height_regime_6 1293 1294 1295 1296 1297 12980.19** 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309(2.78) 1310 1311 1312 1313 1314Height_regime_7 1315 1316 1317 1318 1319 1320 13210.17* 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332(2.21) 1333 1334 1335 1336Height_regime_8 1337 1338 1339 1340 1341 1342 1343 13440.07 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355(0.86) 1356 1357 1358Height_regime_9 1359 1360 1361 1362 1363 1364 1365 1366 13670.14*** 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378(3.63) 1379 1380Height_regime_10 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390-0.03 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401(-0.48) 1402Duration 14030.00*** 14040.00*** 14050.00*** 14060.00*** 14070.00*** 14080.00*** 14090.00*** 14100.00*** 14110.00*** 14120.00*** 1413 1414(3.48) 1415(4.21) 1416(4.06) 1417(4.32) 1418(4.23) 1419(3.76) 1420(4.23) 1421(4.24) 1422(4.42) 1423(4.19) 1424_cons 142511.05*** 10.95*** 10.95*** 10.96*** 10.95*** 10.95*** 10.95*** 10.95*** 10.93*** 10.96*** 1426 1427(503.69) (573.98) (578.31) (573.84) (544.66) (578.05) (572.31) (570.65) (541.08) (566.22) 1428F statistic 142936.53*** 11.02*** 13.09*** 14309.38*** 14319.49*** 143212.84*** 11.39*** 14339.25*** 143415.63*** 9.00*** 1435R-sq 14360.09 14370.03 14380.03 14390.02 14400.02 14410.03 14420.03 14430.02 14440.04 14450.02 1446# 1447765.00 1448765.00 1449765.00 1450765.00 1451765.00 1452765.00 1453765.00 1454765.00 1455765.00 1456765.00 1457Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 145814591460===== PAGE 17 (score 200, numeric-paren 158, table-words 1, var-words 7) =====14611462Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 389-413 1463405 1464 1465Table no. 11 – The output of OLS regressions regarding the impact of the entire range of housing 1466structural attributes on the sale price (duration until transaction completion not included) 1467Variables 1468(1) 1469(2) 1470(3) 1471(4) 1472(5) 1473(6) 1474(7) 1475(8) 1476(9) 1477(10) 1478No_rooms 14790.23*** 14800.24*** 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490(9.19) 1491(9.95) 1492 1493 1494 1495 1496 1497 1498 1499 1500Comfort 15010.34*** 15020.31*** 15030.26*** 15040.14* 15050.20** 15060.12* 15070.26*** 15080.15* 15090.21** 15100.12* 1511 1512(5.79) 1513(5.21) 1514(4.33) 1515(2.54) 1516(3.32) 1517(2.07) 1518(4.28) 1519(2.51) 1520(3.26) 1521(2.06) 1522Floor 1523-0.01* 1524-0.01 1525-0.01 1526-0.01** 1527-0.01 1528-0.01* 1529-0.01 1530-0.01* 1531-0.01 1532-0.01* 1533 1534(-2.03) 1535(-1.49) 1536(-1.45) 1537(-2.66) 1538(-1.37) 1539(-2.42) 1540(-1.20) 1541(-2.09) 1542(-1.35) 1543(-2.19) 1544No_bathrooms 15450.09† 15460.03 15470.07 15480.06 15490.04 15500.03 15510.09† 15520.08† 15530.06 15540.05 1555 1556(1.92) 1557(0.70) 1558(1.52) 1559(1.41) 1560(0.90) 1561(0.80) 1562(1.80) 1563(1.85) 1564(1.14) 1565(1.19) 1566No_balconies 1567-0.03 1568-0.04 1569-0.05 1570-0.02 1571-0.06 1572-0.03 1573-0.08† 1574-0.05 1575-0.09† 1576-0.05 1577 1578(-0.71) 1579(-0.89) 1580(-1.12) 1581(-0.60) 1582(-1.41) 1583(-0.82) 1584(-1.81) 1585(-1.21) 1586(-1.97) 1587(-1.30) 1588No_parking 15890.21** 15900.23** 15910.09 15920.16* 15930.13† 15940.19** 15950.06 15960.16* 15970.09 15980.16* 1599 1600(3.05) 1601(3.15) 1602(1.35) 1603(2.53) 1604(1.71) 1605(2.83) 1606(0.82) 1607(2.43) 1608(1.12) 1609(2.23) 1610Seniority_construction -0.00*** -0.01*** 1611-0.00 1612-0.00 1613-0.00 1614-0.00† 1615-0.00 1616-0.00 1617-0.00 1618-0.00* 1619 1620(-3.76) 1621(-4.57) 1622(-0.43) 1623(-1.20) 1624(-0.89) 1625(-1.83) 1626(-1.03) 1627(-1.55) 1628(-1.34) 1629(-2.02) 1630Seniority_renovation 1631-0.00 1632-0.00 1633-0.00 1634-0.00* 1635-0.00 1636-0.00 1637-0.00 1638-0.00 1639-0.00 1640-0.00 1641 1642(-1.27) 1643(-0.76) 1644(-1.12) 1645(-1.99) 1646(-1.04) 1647(-1.50) 1648(-0.81) 1649(-1.55) 1650(-0.85) 1651(-1.26) 1652Structure 16530.07 16540.09 16550.14† 16560.04 16570.12 16580.04 16590.24** 1660 16610.20* 16620.14† 1663 1664(0.92) 1665(1.09) 1666(1.90) 1667(0.60) 1668(1.51) 1669(0.61) 1670(3.14) 1671 1672(2.45) 1673(1.83) 1674Height_regime_1 1675 16760.05 1677 1678 16790.05 16800.09 1681 1682 1683-0.00 16840.02 1685 1686 1687(0.43) 1688 1689 1690(0.39) 1691(0.85) 1692 1693 1694(-0.04) 1695(0.21) 1696Height_regime_2 1697 16980.02 1699 1700 1701-0.02 17020.05 1703 1704 1705-0.04 17060.01 1707 1708 1709(0.11) 1710 1711 1712(-0.14) 1713(0.35) 1714 1715 1716(-0.24) 1717(0.05) 1718Height_regime_3 1719 17200.27† 1721 1722 17230.09 17240.22† 1725 1726 1727-0.05 17280.08 1729 1730 1731(1.86) 1732 1733 1734(0.65) 1735(1.67) 1736 1737 1738(-0.34) 1739(0.54) 1740Height_regime_4 1741 17420.03 1743 1744 1745-0.01 17460.04 1747 1748 1749-0.09 1750-0.04 1751 1752 1753(0.20) 1754 1755 1756(-0.07) 1757(0.36) 1758 1759 1760(-0.61) 1761(-0.34) 1762Height_regime_5 1763 17640.09 1765 1766 17670.09 17680.09 1769 1770 17710.04 17720.03 1773 1774 1775(0.69) 1776 1777 1778(0.74) 1779(0.81) 1780 1781 1782(0.33) 1783(0.25) 1784Height_regime_6 1785 17860.12 1787 1788 17890.03 17900.08 1791 1792 1793-0.00 17940.02 1795 1796 1797(0.75) 1798 1799 1800(0.18) 1801(0.62) 1802 1803 1804(-0.02) 1805(0.17) 1806Height_regime_7 1807 18080.24 1809 1810 1811-0.09 18120.11 1813 1814 1815-0.10 18160.03 1817 1818 1819(1.44) 1820 1821 1822(-0.56) 1823(0.74) 1824 1825 1826(-0.58) 1827(0.16) 1828Height_regime_8 1829 18300.00 1831 1832 18330.00 18340.00 1835 1836 18370.00 18380.00 1839 1840 1841(.) 1842 1843 1844(.) 1845(.) 1846 1847 1848(.) 1849(.) 1850Height_regime_9 1851 18520.19 1853 1854 18550.18 18560.19† 1857 1858 18590.13 18600.13 1861 1862 1863(1.59) 1864 1865 1866(1.50) 1867(1.76) 1868 1869 1870(1.06) 1871(1.13) 1872Height_regime_10 1873 18740.25† 1875 1876 18770.22 18780.27* 1879 1880 18810.16 18820.19 1883 1884 1885(1.82) 1886 1887 1888(1.60) 1889(2.16) 1890 1891 1892(1.12) 1893(1.45) 1894S_useful 1895 1896 18970.01*** 18980.03*** 18990.01*** 19000.03*** 1901 1902 1903 1904 1905 1906 1907 1908(9.63) 1909(9.82) 1910(10.24) 1911(9.54) 1912 1913 1914 1915 1916S_useful_sq 1917 1918 1919 1920-0.00*** 1921 1922-0.00*** 1923 1924 1925 1926 1927 1928 1929 1930 1931(-6.58) 1932 1933(-6.15) 1934 1935 1936 1937 1938S_constructed 1939 1940 1941 1942 1943 1944 19450.01*** 19460.03*** 19470.01*** 19480.02*** 1949 1950 1951 1952 1953 1954 1955 1956(8.86) 1957(9.93) 1958(9.37) 1959(9.28) 1960S_constructed_sq 1961 1962 1963 1964 1965 1966 1967 1968-0.00*** 1969 1970-0.00*** 1971 1972 1973 1974 1975 1976 1977 1978 1979(-6.82) 1980 1981(-5.77) 1982_cons 198310.00*** 9.96*** 19849.92*** 19859.49*** 19869.91*** 19879.42*** 19889.90*** 19899.61*** 19909.97*** 19919.53*** 1992 1993(65.73) (53.81) (66.69) (64.32) (54.27) (51.69) (64.74) (69.58) (52.63) (50.59) 1994Turning points 1995 1996 1997 1998116,15 1999 2000117,77 2001 2002126,04 2003 2004134,33 2005F statistic 200638.18*** 22.13*** 40.08*** 49.78*** 22.84*** 28.81*** 36.84*** 50.63*** 20.78*** 25.56*** 2007R-sq 20080.68 20090.72 20100.69 20110.75 20120.73 20130.78 20140.67 20150.72 20160.71 20170.76 2018# 2019173.00 2020173.00 2021173.00 2022173.00 2023173.00 2024173.00 2025173.00 2026183.00 2027173.00 2028173.00 2029Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 2030 2031 203220332034===== PAGE 18 (score 205, numeric-paren 161, table-words 1, var-words 7) =====20352036406 2037Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 2038 2039Table no. 12 – The output of OLS regressions regarding the impact of the entire range of 2040housing structural attributes on the sale price (duration until transaction completion included) 2041Variables 2042(1) 2043(2) 2044(3) 2045(4) 2046(5) 2047(6) 2048(7) 2049(8) 2050(9) 2051(10) 2052No_rooms 20530.23*** 20540.25*** 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064(9.05) 2065(9.70) 2066 2067 2068 2069 2070 2071 2072 2073 2074Comfort 20750.34*** 20760.31*** 20770.26*** 20780.14* 20790.20** 20800.12* 20810.26*** 20820.14* 20830.21** 20840.12* 2085 2086(5.81) 2087(5.23) 2088(4.32) 2089(2.47) 2090(3.30) 2091(2.05) 2092(4.26) 2093(2.50) 2094(3.25) 2095(2.03) 2096Floor 2097-0.01* 2098-0.01 2099-0.01 2100-0.01** 2101-0.01 2102-0.01* 2103-0.01 2104-0.01* 2105-0.01 2106-0.01* 2107 2108(-2.01) 2109(-1.47) 2110(-1.44) 2111(-2.64) 2112(-1.36) 2113(-2.41) 2114(-1.20) 2115(-1.98) 2116(-1.35) 2117(-2.18) 2118No_bathrooms 21190.08 21200.03 21210.07 21220.05 21230.04 21240.03 21250.09† 21260.07 21270.06 21280.05 2129 2130(1.65) 2131(0.55) 2132(1.48) 2133(1.14) 2134(0.90) 2135(0.64) 2136(1.79) 2137(1.59) 2138(1.15) 2139(1.04) 2140No_balconies 2141-0.04 2142-0.04 2143-0.05 2144-0.03 2145-0.06 2146-0.04 2147-0.08† 2148-0.06 2149-0.09† 2150-0.06 2151 2152(-0.82) 2153(-0.97) 2154(-1.12) 2155(-0.73) 2156(-1.39) 2157(-0.92) 2158(-1.78) 2159(-1.38) 2160(-1.92) 2161(-1.40) 2162No_parking 21630.21** 21640.23** 21650.09 21660.16** 21670.13† 21680.20** 21690.06 21700.16* 21710.09 21720.17* 2173 2174(3.11) 2175(3.19) 2176(1.35) 2177(2.63) 2178(1.69) 2179(2.92) 2180(0.81) 2181(2.52) 2182(1.10) 2183(2.30) 2184Seniority_construction -0.00*** -0.01*** 2185-0.00 2186-0.00 2187-0.00 2188-0.00† 2189-0.00 2190-0.00† 2191-0.00 2192-0.00* 2193 2194(-3.87) 2195(-4.61) 2196(-0.43) 2197(-1.37) 2198(-0.88) 2199(-1.91) 2200(-1.01) 2201(-1.73) 2202(-1.32) 2203(-2.09) 2204Seniority_renovation 2205-0.00 2206-0.00 2207-0.00 2208-0.00† 2209-0.00 2210-0.00 2211-0.00 2212-0.00 2213-0.00 2214-0.00 2215 2216(-1.18) 2217(-0.70) 2218(-1.10) 2219(-1.89) 2220(-1.03) 2221(-1.43) 2222(-0.81) 2223(-1.40) 2224(-0.86) 2225(-1.19) 2226Structure 22270.06 22280.08 22290.14† 22300.02 22310.12 22320.03 22330.25** 2234 22350.21* 22360.12 2237 2238(0.72) 2239(0.91) 2240(1.85) 2241(0.32) 2242(1.48) 2243(0.34) 2244(3.11) 2245 2246(2.44) 2247(1.57) 2248Duration 2249-0.00 2250-0.00 2251-0.00 2252-0.00 22530.00 2254-0.00 22550.00 2256-0.00† 22570.00 2258-0.00 2259 2260(-0.93) 2261(-0.69) 2262(-0.11) 2263(-1.37) 2264(0.05) 2265(-1.08) 2266(0.09) 2267(-1.73) 2268(0.19) 2269(-0.97) 2270Height_regime_1 2271 22720.06 2273 2274 22750.05 22760.11 2277 2278 2279-0.01 22800.04 2281 2282 2283(0.51) 2284 2285 2286(0.38) 2287(1.00) 2288 2289 2290(-0.06) 2291(0.34) 2292Height_regime_2 2293 22940.03 2295 2296 2297-0.02 22980.06 2299 2300 2301-0.04 23020.02 2303 2304 2305(0.18) 2306 2307 2308(-0.15) 2309(0.47) 2310 2311 2312(-0.26) 2313(0.15) 2314Height_regime_3 2315 23160.27† 2317 2318 23190.09 23200.22† 2321 2322 2323-0.05 23240.08 2325 2326 2327(1.89) 2328 2329 2330(0.65) 2331(1.72) 2332 2333 2334(-0.34) 2335(0.56) 2336Height_regime_4 2337 23380.04 2339 2340 2341-0.01 23420.06 2343 2344 2345-0.09 2346-0.03 2347 2348 2349(0.28) 2350 2351 2352(-0.08) 2353(0.49) 2354 2355 2356(-0.62) 2357(-0.23) 2358Height_regime_5 2359 23600.10 2361 2362 23630.09 23640.11 2365 2366 23670.04 23680.04 2369 2370 2371(0.78) 2372 2373 2374(0.72) 2375(0.95) 2376 2377 2378(0.30) 2379(0.37) 2380Height_regime_6 2381 23820.12 2383 2384 23850.03 23860.09 2387 2388 2389-0.00 23900.03 2391 2392 2393(0.78) 2394 2395 2396(0.18) 2397(0.67) 2398 2399 2400(-0.02) 2401(0.20) 2402Height_regime_7 2403 24040.25 2405 2406 2407-0.09 24080.13 2409 2410 2411-0.10 24120.04 2413 2414 2415(1.52) 2416 2417 2418(-0.56) 2419(0.87) 2420 2421 2422(-0.59) 2423(0.27) 2424Height_regime_8 2425 24260.00 2427 2428 24290.00 24300.00 2431 2432 24330.00 24340.00 2435 2436 2437(.) 2438 2439 2440(.) 2441(.) 2442 2443 2444(.) 2445(.) 2446Height_regime_9 2447 24480.20 2449 2450 24510.18 24520.20† 2453 2454 24550.13 24560.14 2457 2458 2459(1.65) 2460 2461 2462(1.48) 2463(1.87) 2464 2465 2466(1.03) 2467(1.22) 2468Height_regime_10 2469 24700.26† 2471 2472 24730.22 24740.28* 2475 2476 24770.16 24780.20 2479 2480 2481(1.86) 2482 2483 2484(1.59) 2485(2.25) 2486 2487 2488(1.10) 2489(1.52) 2490S_useful 2491 2492 24930.01*** 24940.03*** 24950.01*** 24960.03*** 2497 2498 2499 2500 2501 2502 2503 2504(9.42) 2505(9.89) 2506(9.96) 2507(9.53) 2508 2509 2510 2511 2512S_useful_sq 2513 2514 2515 2516-0.00*** 2517 2518-0.00*** 2519 2520 2521 2522 2523 2524 2525 2526 2527(-6.74) 2528 2529(-6.25) 2530 2531 2532 2533 2534S_constructed 2535 2536 2537 2538 2539 2540 25410.01*** 25420.03*** 25430.01*** 25440.03*** 2545 2546 2547 2548 2549 2550 2551 2552(8.65) 2553(10.13) 2554(9.09) 2555(9.20) 2556S_constructed_sq 2557 2558 2559 2560 2561 2562 2563 2564-0.00*** 2565 2566-0.00*** 2567 2568 2569 2570 2571 2572 2573 2574 2575(-7.03) 2576 2577(-5.84) 2578_cons 257910.03*** 9.97*** 25809.92*** 25819.50*** 25829.91*** 25839.42*** 25849.90*** 25859.61*** 25869.97*** 25879.53*** 2588 2589(64.51) (53.63) (65.47) (64.39) (53.99) (51.67) (63.55) (69.98) (52.33) (50.55) 2590Turning points 2591 2592 2593 2594115,63 2595 2596117,20 2597 2598125,93 2599 2600133,54 2601F statistic 260234.42*** 20.92*** 35.86*** 45.67*** 21.50*** 27.45*** 32.95*** 46.40*** 19.56*** 24.32*** 2603R-sq 26040.68 26050.72 26060.69 26070.76 26080.73 26090.78 26100.67 26110.73 26120.71 26130.76 2614# 2615173.00 2616173.00 2617173.00 2618173.00 2619173.00 2620173.00 2621173.00 2622183.00 2623173.00 2624173.00 2625Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 2626 262726282629===== PAGE 19 (score 132, numeric-paren 94, table-words 2, var-words 12) =====26302631Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 389-413 2632407 2633 2634Further, when adding the squared term of the useful surface (Eq. 4 and Eq. 6) and 2635constructed surface (Eq. 8 and Eq. 10), we notice a concave association between the latter and 2636the price of the apartment, similar to Ooi et al. (2014), Brecard et al. (2018). Consequently, 2637the threshold for useful surface is located between 115-117 square meters and between 125-2638134 squared meter in case of constructed surface. 2639 26404.3 Robustness checks 2641 2642A broad description of various distributions may be gathered via quantile regression 2643which considers the overall conditional distribution, not just the conditional mean effect (Wen 2644et al., 2019).As different to linear regression, which estimates a conditional mean function, 2645quantile regression estimates a conditional quantile function, in which a quantile of the 2646response variable’s conditional distribution is showed as a function of covariates (Liao and 2647Wang, 2012). As well, quantile regression estimates are more informative and robust to 2648outliers (Zhang and Yi, 2017). The influence of the entire range of housing structural attributes 2649on the sale price, except constructed surface, estimated via quantile regressions is exhibited 2650in Table no. 13. 2651 2652Table no. 13 – The outcomes of quantile regressions towards the impact of the entire range of 2653housing structural attributes (except constructed surface) on the sale price 2654Variables 2655(1) 2656(2) 2657(3) 2658(4) 2659(5) 2660(6) 2661(7) 2662(8) 2663(9) 2664Quantile 2665.1 2666.2 2667.3 2668.4 2669.5 2670.6 2671.7 2672.8 2673.9 2674S_useful 26750.01*** 26760.01*** 26770.01*** 26780.01*** 26790.01*** 26800.01*** 26810.01*** 26820.01*** 26830.01*** 2684 2685(6.85) 2686(6.34) 2687(9.78) 2688(14.08) (14.41) 2689(9.20) 2690(6.82) 2691(6.47) 2692(5.62) 2693Comfort 26940.14 26950.12 26960.18** 26970.18*** 26980.20*** 26990.24*** 27000.26** 27010.29** 27020.30* 2703 2704(1.51) 2705(1.26) 2706(2.73) 2707(3.91) 2708(4.09) 2709(3.86) 2710(3.25) 2711(2.91) 2712(2.57) 2713Floor 27140.00 2715-0.00 2716-0.01 2717-0.00 2718-0.01 2719-0.00 2720-0.01 2721-0.01 2722-0.02 2723 2724(0.30) 2725(-0.42) 2726(-0.95) 2727(-0.65) 2728(-1.30) 2729(-0.55) 2730(-1.04) 2731(-1.52) 2732(-1.60) 2733No_bathrooms 27340.03 27350.10 27360.07 27370.07* 27380.05 27390.12* 27400.13* 27410.07 27420.07 2743 2744(0.35) 2745(1.25) 2746(1.31) 2747(2.07) 2748(1.30) 2749(2.32) 2750(2.10) 2751(0.96) 2752(0.72) 2753No_balconies 27540.01 27550.00 27560.01 2757-0.02 2758-0.02 2759-0.04 2760-0.05 2761-0.06 2762-0.02 2763 2764(0.11) 2765(0.05) 2766(0.12) 2767(-0.69) 2768(-0.59) 2769(-0.95) 2770(-0.92) 2771(-0.88) 2772(-0.23) 2773No_parking 2774-0.07 27750.06 27760.04 27770.03 27780.02 27790.10 27800.09 27810.06 27820.10 2783 2784(-0.59) 2785(0.50) 2786(0.53) 2787(0.65) 2788(0.27) 2789(1.37) 2790(0.92) 2791(0.51) 2792(0.76) 2793Seniority_construction 27940.00 2795-0.00 27960.00 2797-0.00 2798-0.00 27990.00 2800-0.00 28010.00 2802-0.00 2803 2804(0.13) 2805(-0.45) 2806(0.02) 2807(-0.46) 2808(-0.44) 2809(0.16) 2810(-0.37) 2811(0.53) 2812(-0.11) 2813Seniority_renovation 2814-0.00 2815-0.00 2816-0.00* 2817-0.01** 2818-0.00* 2819-0.00† 2820-0.00 2821-0.00 28220.00 2823 2824(-0.96) 2825(-1.27) 2826(-1.98) 2827(-3.34) 2828(-2.44) 2829(-1.91) 2830(-1.00) 2831(-1.05) 2832(0.13) 2833Structure 28340.87*** 2835-0.02 28360.06 28370.04 28380.07 28390.14† 28400.08 2841-0.09 2842-0.02 2843 2844(7.22) 2845(-0.16) 2846(0.76) 2847(0.74) 2848(1.12) 2849(1.78) 2850(0.82) 2851(-0.75) 2852(-0.16) 2853Duration 2854-0.00 2855-0.00 2856-0.00 2857-0.00 2858-0.00 2859-0.00 2860-0.00 2861-0.00 2862-0.00 2863 2864(-1.05) 2865(-0.34) 2866(-0.02) 2867(-0.24) 2868(-0.86) 2869(-0.06) 2870(-0.13) 2871(-0.62) 2872(-0.61) 2873_cons 28749.09*** 28759.97*** 28769.90*** 28779.97*** 28789.97*** 28799.83*** 28809.98*** 288110.20*** 10.15*** 2882 2883(37.81) (40.26) (60.83) (86.47) (82.78) (62.00) (49.16) 2884(41.19) 2885(34.79) 2886Pseudo R-sq 28870.4887 28880.5165 28890.5270 28900.5213 28910.5132 28920.5084 28930.4991 28940.4787 28950.4593 2896# 2897173.00 2898173.00 2899173.00 2900173.00 2901173.00 2902173.00 2903173.00 2904173.00 2905173.00 2906Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 2907 2908Thus, the outcomes provide support for a positive influence of useful area (Eq. 1 – Eq. 29099) as in Liao and Wang (2012), McCord et al. (2018), comfort (Eq. 3 – Eq. 9), number of 2910bathrooms (Eq. 4, Eq. 6 and, Eq. 7) similar Zhang and Leonard (2014), structure (Eq. 1 and 291129122913===== PAGE 20 (score 153, numeric-paren 108, table-words 5, var-words 11) =====29142915408 2916Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 2917 2918Eq. 6). The seniority from renovation reveals a negative influence, as opposed to Zhang and 2919Leonard (2014). In addition, the floor level does not reveal a statistically significant relation 2920with sale price, on the contrary to Liao and Wang (2012). Besides, opposite to Wen et al. 2921(2019), the number of years since construction is not statistically significant. 2922Table no. 14 shows the quantile regression outcomes when adding the squared term of 2923useful surface. Therefore, similar to Kim et al., 2015, the non-linear relationship between 2924surface and sale price is confirmed. The identified thresholds are decreasing gradually from 2925the first to the last quantile, being located between 104 and 117 square meters. 2926 2927Table no. 14 – The outcomes of quadratic quantiles regressions towards the impact of the entire 2928range of housing structural attributes (except constructed surface) on the sale price 2929Variables 2930(1) 2931(2) 2932(3) 2933(4) 2934(5) 2935(6) 2936(7) 2937(8) 2938(9) 2939Quantile 2940.1 2941.2 2942.3 2943.4 2944.5 2945.6 2946.7 2947.8 2948.9 2949S_useful 29500.03*** 29510.03*** 29520.03*** 29530.03*** 29540.03*** 29550.03*** 29560.03*** 29570.04*** 29580.04*** 2959 2960(5.56) 2961(9.94) 2962(10.65) 2963(10.89) 2964(8.98) 2965(8.31) 2966(6.46) 2967(6.84) 2968(7.50) 2969S_useful_sq 2970-0.00*** 2971-0.00*** 2972-0.00*** 2973-0.00*** 2974-0.00*** 2975-0.00*** 2976-0.00*** 2977-0.00*** 2978-0.00*** 2979 2980(-3.73) 2981(-6.99) 2982(-7.35) 2983(-7.54) 2984(-6.15) 2985(-5.83) 2986(-4.57) 2987(-4.92) 2988(-5.69) 2989Comfort 29900.11 29910.10† 29920.11* 29930.10* 29940.13* 29950.16* 29960.18* 29970.16 29980.03 2999 3000(1.14) 3001(1.82) 3002(2.15) 3003(1.98) 3004(2.27) 3005(2.46) 3006(2.15) 3007(1.65) 3008(0.24) 3009Floor 3010-0.01 3011-0.01* 3012-0.01** 3013-0.01 3014-0.00 3015-0.00 3016-0.01 3017-0.02† 3018-0.03** 3019 3020(-1.24) 3021(-2.02) 3022(-2.73) 3023(-1.50) 3024(-0.15) 3025(-0.68) 3026(-1.37) 3027(-1.88) 3028(-3.32) 3029No_bathrooms 30300.08 30310.08* 30320.08* 30330.08* 30340.10* 30350.12* 30360.11† 30370.03 30380.04 3039 3040(1.17) 3041(2.09) 3042(2.19) 3043(2.13) 3044(2.38) 3045(2.47) 3046(1.75) 3047(0.48) 3048(0.47) 3049No_balconies 3050-0.05 3051-0.00 3052-0.02 3053-0.01 3054-0.01 3055-0.02 3056-0.03 3057-0.05 3058-0.02 3059 3060(-0.86) 3061(-0.02) 3062(-0.54) 3063(-0.20) 3064(-0.37) 3065(-0.56) 3066(-0.45) 3067(-0.72) 3068(-0.30) 3069No_parking 30700.11 30710.23*** 30720.18*** 30730.25*** 30740.23*** 30750.22** 30760.29** 30770.27* 30780.12 3079 3080(1.05) 3081(3.90) 3082(3.38) 3083(4.65) 3084(3.59) 3085(3.01) 3086(3.20) 3087(2.56) 3088(1.00) 3089Seniority_construction 3090-0.00 3091-0.00† 3092-0.00† 3093-0.00† 3094-0.00 3095-0.00 30960.00 30970.00 30980.00 3099 3100(-1.00) 3101(-1.87) 3102(-1.74) 3103(-1.73) 3104(-0.60) 3105(-0.19) 3106(0.43) 3107(1.29) 3108(0.26) 3109Seniority_renovation 3110-0.01† 3111-0.01** 3112-0.01** 3113-0.00** 3114-0.00* 3115-0.00† 3116-0.00 3117-0.00 3118-0.00 3119 3120(-1.83) 3121(-2.97) 3122(-3.05) 3123(-2.93) 3124(-2.25) 3125(-1.66) 3126(-0.94) 3127(-1.36) 3128(-0.84) 3129Structure 3130-0.04 3131-0.01 31320.02 31330.04 31340.09 31350.06 31360.06 3137-0.10 3138-0.07 3139 3140(-0.33) 3141(-0.21) 3142(0.29) 3143(0.60) 3144(1.19) 3145(0.81) 3146(0.58) 3147(-0.86) 3148(-0.55) 3149Duration 3150-0.00 3151-0.00 3152-0.00 3153-0.00 3154-0.00 3155-0.00 3156-0.00 3157-0.00 3158-0.00† 3159 3160(-1.46) 3161(-0.94) 3162(-0.82) 3163(-0.70) 3164(-0.09) 3165(-0.59) 3166(-0.87) 3167(-1.39) 3168(-1.73) 3169_cons 31709.58*** 31719.38*** 31729.45*** 31739.34*** 31749.26*** 31759.27*** 31769.25*** 31779.36*** 31789.52*** 3179 3180(39.52) 3181(66.34) 3182(73.17) 3183(72.48) 3184(60.77) 3185(54.64) 3186(42.84) 3187(37.43) 3188(34.37) 3189Turning points 3190117.44 3191112.23 3192114.21 3193113.81 3194115.25 3195112.43 3196111.68 3197109.68 3198104.02 3199Pseudo R-sq 32000.5844 32010.6082 32020.5934 32030.5710 32040.5546 32050.5453 32060.5363 32070.5186 32080.5181 3209# 3210173.00 3211173.00 3212173.00 3213173.00 3214173.00 3215173.00 3216173.00 3217173.00 3218173.00 3219Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 3220 3221The impact of the entire range of housing structural attributes on the sale price, except 3222useful surface, assessed via quantile regressions is exhibited in Table no. 15. The outcomes 3223confirms the positive influence of constructed surface (Eq. 1 – Eq. 9), comfort (Eq. 2 – Eq. 32249), the number of bathrooms (Eq. 3, Eq. 6 and, Eq. 7) and structure (Eq. 1 – Eq. 5) on sale 3225price. The number of balconies (Eq. 1, Eq. 5 and, Eq. 6) shows a negative, but weak, negative 3226impact on dwellings’ price. In case of the number of parking (Eq. 1 and Eq. 5) the influence 3227is mixed, but its statistical significance is weak. 3228Table no. 16 reveals the output of quantile regressions when adding the squared term of 3229constructed surface. Thereby, kindred with the outcomes from Table no. 14, the non-linear 3230association is consolidated. The computed thresholds are located between 117 and 145 square 3231meters. 323232333234===== PAGE 21 (score 269, numeric-paren 210, table-words 2, var-words 9) =====32353236Scientific Annals of Economics and Business, 2019, Volume 66, Issue 3, pp. 389-413 3237409 3238 3239Table no. 15 – The outcomes of quantile regressions towards the impact of the entire range of 3240housing structural attributes (except useful surface) on the sale price 3241Variables 3242(1) 3243(2) 3244(3) 3245(4) 3246(5) 3247(6) 3248(7) 3249(8) 3250(9) 3251Quantile 3252.1 3253.2 3254.3 3255.4 3256.5 3257.6 3258.7 3259.8 3260.9 3261S_constructed 32620.01*** 32630.01*** 32640.01*** 32650.01*** 32660.01*** 32670.01*** 32680.01*** 32690.01*** 32700.01*** 3271 3272(6.74) 3273(6.59) 3274(8.25) 3275(9.41) 3276(9.90) 3277(8.45) 3278(7.48) 3279(6.12) 3280(3.84) 3281Comfort 32820.14 32830.16† 32840.20** 32850.20** 32860.21** 32870.25*** 32880.25*** 32890.30** 32900.31* 3291 3292(1.42) 3293(1.81) 3294(2.80) 3295(3.21) 3296(3.25) 3297(3.65) 3298(3.38) 3299(3.23) 3300(2.08) 3301Floor 33020.00 3303-0.00 3304-0.01 3305-0.00 3306-0.00 3307-0.00 3308-0.01 3309-0.01 3310-0.02 3311 3312(0.06) 3313(-0.42) 3314(-1.41) 3315(-0.28) 3316(-0.18) 3317(-0.62) 3318(-1.08) 3319(-1.40) 3320(-1.24) 3321No_bathrooms 33220.02 33230.05 33240.10† 33250.06 33260.06 33270.12* 33280.17** 33290.11 33300.11 3331 3332(0.23) 3333(0.79) 3334(1.78) 3335(1.33) 3336(1.12) 3337(2.25) 3338(2.84) 3339(1.44) 3340(0.92) 3341No_balconies 3342-0.12† 3343-0.02 3344-0.06 3345-0.07 3346-0.10* 3347-0.08† 3348-0.07 3349-0.03 3350-0.01 3351 3352(-1.67) 3353(-0.37) 3354(-1.08) 3355(-1.44) 3356(-2.10) 3357(-1.67) 3358(-1.29) 3359(-0.39) 3360(-0.08) 3361No_parking 3362-0.26* 3363-0.01 3364-0.09 33650.03 33660.13† 33670.07 33680.04 33690.05 33700.06 3371 3372(-2.14) 3373(-0.12) 3374(-1.11) 3375(0.38) 3376(1.76) 3377(0.90) 3378(0.43) 3379(0.42) 3380(0.34) 3381Seniority_construction 33820.00 3383-0.00 3384-0.00 3385-0.00 3386-0.00 3387-0.00 3388-0.00 3389-0.00 3390-0.00 3391 3392(0.12) 3393(-0.15) 3394(-0.34) 3395(-1.00) 3396(-1.36) 3397(-0.59) 3398(-0.71) 3399(-0.31) 3400(-0.01) 3401Seniority_renovation 3402-0.01 3403-0.00 3404-0.00 3405-0.00 3406-0.00 3407-0.00 3408-0.00 3409-0.00 34100.00 3411 3412(-1.42) 3413(-1.31) 3414(-1.20) 3415(-1.21) 3416(-0.89) 3417(-1.04) 3418(-1.04) 3419(-1.30) 3420(0.53) 3421Structure 34220.84*** 34230.46*** 34240.25** 34250.25** 34260.27** 34270.13 34280.09 34290.05 34300.07 3431 3432(6.52) 3433(4.17) 3434(2.79) 3435(3.17) 3436(3.30) 3437(1.52) 3438(0.99) 3439(0.43) 3440(0.37) 3441Duration 3442-0.00 34430.00 34440.00 3445-0.00 3446-0.00 3447-0.00 3448-0.00 34490.00 3450-0.00 3451 3452(-0.37) 3453(0.75) 3454(0.31) 3455(-0.12) 3456(-0.44) 3457(-0.07) 3458(-0.06) 3459(0.29) 3460(-0.10) 3461_cons 34629.45*** 34639.60*** 34649.91*** 34659.87*** 34669.79*** 34679.93*** 346810.01*** 346910.08*** 347010.06*** 3471 3472(37.15) 3473(43.72) 3474(55.86) 3475(63.36) 3476(60.99) 3477(57.76) 3478(53.26) 3479(43.33) 3480(26.71) 3481Pseudo R-sq 34820.4729 34830.4941 34840.4960 34850.4844 34860.4753 34870.4788 34880.4792 34890.4667 34900.4491 3491# 3492173.00 3493173.00 3494173.00 3495173.00 3496173.00 3497173.00 3498173.00 3499173.00 3500173.00 3501Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 3502 3503Table no. 16 – The outcomes of quadratic quantile regressions towards the impact of the entire 3504range of housing structural attributes (except useful surface) on the sale price 3505Variables 3506(1) 3507(2) 3508(3) 3509(4) 3510(5) 3511(6) 3512(7) 3513(8) 3514(9) 3515Quantile 3516.1 3517.2 3518.3 3519.4 3520.5 3521.6 3522.7 3523.8 3524.9 3525S_constructed 35260.03*** 35270.02*** 35280.02*** 35290.02*** 35300.03*** 35310.03*** 35320.02*** 35330.03*** 35340.03*** 3535 3536(7.03) 3537(8.59) 3538(7.78) 3539(8.25) 3540(7.54) 3541(8.96) 3542(6.11) 3543(7.48) 3544(6.27) 3545S_constructed_sq 3546-0.00*** 3547-0.00*** 3548-0.00*** 3549-0.00*** 3550-0.00*** 3551-0.00*** 3552-0.00*** 3553-0.00*** 3554-0.00*** 3555 3556(-4.70) 3557(-5.31) 3558(-4.66) 3559(-5.28) 3560(-5.13) 3561(-6.43) 3562(-4.49) 3563(-5.30) 3564(-4.63) 3565Comfort 35660.04 35670.09 35680.14* 35690.14* 35700.15* 35710.17** 35720.21* 35730.19* 35740.13 3575 3576(0.47) 3577(1.59) 3578(2.31) 3579(2.45) 3580(2.04) 3581(2.78) 3582(2.42) 3583(2.19) 3584(1.08) 3585Floor 3586-0.00 3587-0.01† 3588-0.01* 3589-0.01 35900.00 3591-0.00 3592-0.01 3593-0.02* 3594-0.02* 3595 3596(-0.34) 3597(-1.72) 3598(-2.09) 3599(-1.12) 3600(0.10) 3601(-0.58) 3602(-1.45) 3603(-2.18) 3604(-2.32) 3605No_bathrooms 36060.07 36070.10* 36080.09† 36090.10* 36100.13* 36110.15** 36120.14* 36130.07 36140.11 3615 3616(1.13) 3617(2.45) 3618(1.97) 3619(2.21) 3620(2.37) 3621(3.27) 3622(2.09) 3623(1.08) 3624(1.20) 3625No_balconies 3626-0.12* 3627-0.03 3628-0.05 3629-0.03 3630-0.08 3631-0.02 3632-0.01 3633-0.04 3634-0.06 3635 3636(-2.09) 3637(-0.86) 3638(-1.22) 3639(-0.71) 3640(-1.60) 3641(-0.55) 3642(-0.09) 3643(-0.69) 3644(-0.68) 3645No_parking 36460.15 36470.10 36480.06 36490.23*** 36500.22** 36510.21** 36520.26** 36530.17† 36540.06 3655 3656(1.58) 3657(1.49) 3658(0.84) 3659(3.48) 3660(2.73) 3661(2.99) 3662(2.68) 3663(1.73) 3664(0.42) 3665Seniority_construction 3666-0.00* 3667-0.00 3668-0.00 3669-0.00 3670-0.00 3671-0.00 36720.00 36730.00 36740.00 3675 3676(-2.06) 3677(-1.15) 3678(-1.13) 3679(-1.14) 3680(-1.59) 3681(-0.60) 3682(0.03) 3683(1.58) 3684(0.60) 3685Seniority_renovation 3686-0.00† 3687-0.00† 3688-0.00† 3689-0.00* 3690-0.00 3691-0.00 3692-0.00 3693-0.01† 3694-0.00 3695 3696(-1.75) 3697(-1.96) 3698(-1.72) 3699(-2.35) 3700(-1.19) 3701(-1.58) 3702(-0.99) 3703(-1.78) 3704(-0.91) 3705Structure 37060.11 37070.21** 37080.23** 37090.17* 37100.05 37110.06 37120.04 37130.03 37140.04 3715 3716(1.10) 3717(2.92) 3718(3.17) 3719(2.44) 3720(0.56) 3721(0.83) 3722(0.37) 3723(0.31) 3724(0.24) 3725Duration 3726-0.00 37270.00 3728-0.00 3729-0.00 3730-0.00 3731-0.00 37320.00 3733-0.00 3734-0.00 3735 3736(-0.68) 3737(0.27) 3738(-0.01) 3739(-0.86) 3740(-0.73) 3741(-0.52) 3742(0.02) 3743(-1.29) 3744(-0.96) 3745_cons 37469.47*** 37479.40*** 37489.51*** 37499.38*** 37509.46*** 37519.34*** 37529.37*** 37539.35*** 37549.50*** 3755 3756(44.30) 3757(63.29) 3758(61.86) 3759(62.97) 3760(51.45) 3761(58.64) 3762(41.71) 3763(40.68) 3764(30.96) 3765Turning points 3766130.35 3767141.10 3768145.46 3769136.23 3770127.84 3771121.34 3772118.38 3773122.88 3774117.77 3775Pseudo R-sq 37760.5574 37770.5769 37780.5539 37790.5284 37800.5185 37810.5133 37820.5152 37830.5028 37840.5006 3785# 3786173.00 3787173.00 3788173.00 3789173.00 3790173.00 3791173.00 3792173.00 3793173.00 3794173.00 3795Source: Authors’ computations.† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001 379637973798===== PAGE 22 (score 19, numeric-paren 0, table-words 0, var-words 18) =====37993800410 3801Zilișteanu, I. R., Muntean, R., Gherghina, Ș. C., Vintilă, G., Barbu, T. C. 3802 38035. CONCLUDING REMARKS 3804 3805Current study aimed to explore the influence of the structural attributes related to an 3806apartment on its sale price in Bucharest, Romania, over 2014-2018. Several structural 3807attributes were selected, among which OLS regression outcomes provide support for a 3808positive impact, respectively the number of rooms, comfort, the number of bathrooms, the 3809number of parking, the number of balconies and, structure. On the contrary, in case of floor 3810level and seniority from construction, the OLS estimates reveals a negative relationship. 3811Nevertheless, we notice concave links between useful surface and sale price, as well as 3812between constructed surface and sale price. When estimating via quantile regressions, the 3813associations are reinforced. 3814The main limitation of current study is driven by OLS estimation regardless of the spatial 3815effects (Z. Z. Huang et al., 2017). The issue with traditional hedonic pricing models is the 3816insufficiency towards depicting the fundamental link between property price and geographical 3817spatial interaction (Hui et al., 2012). Therefore, as future research avenues, spatial 3818econometric modeling will be considered. 3819 3820Acknowledgements 3821The research is supported by RMI project „Ansamblu de indici imobiliari structurați pentru piața 3822românească” - Proiectul este cofinanțat prin Programul Operațional Competitivitate 2014-2020, Axa 3823Prioritară 2 „Tehnologia Informației și Comunicațiilor (TIC) pentru o economie digitală competitivă”, 3824Obiectiv specific 2.2 „Creșterea contribuției sectorului TIC pentru competitivitatea economică”, 3825Acțiunea 2.2.1 „Sprijinirea creșterii valorii adăugate generate de sectorul TIC și a inovării în domeniu 3826prin dezvoltarea de clustere”, Prioritatea de Investiții „PI2.2 Dezvoltarea produselor și serviciilor, a 3827comerțului electronic, a comerțului electronic și a cererii de TIC” - cod mySMIS 115645. 3828 3829 3830References 3831 3832Baranzini, A., and Schaerer, C., 2011. 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