1{2 "status": "ok",3 "message-type": "work",4 "message-version": "1.0.0",5 "message": {6 "indexed": {7 "date-parts": [8 [9 2026,10 9,11 112 ]13 ],14 "date-time": "2026-09-01T03:12:33Z",15 "timestamp": 1788232353281,16 "version": "build-2803163510"17 },18 "reference-count": 57,19 "publisher": "MDPI AG",20 "issue": "2",21 "license": [22 {23 "start": {24 "date-parts": [25 [26 2025,27 1,28 3129 ]30 ],31 "date-time": "2025-01-31T00:00:00Z",32 "timestamp": 173828160000033 },34 "content-version": "vor",35 "delay-in-days": 0,36 "URL": "https://creativecommons.org/licenses/by/4.0/"37 }38 ],39 "content-domain": {40 "domain": [],41 "crossmark-restriction": false42 },43 "short-container-title": [44 "Urban Science"45 ],46 "abstract": "<jats:p>Understanding the determinants of housing price movements is an ongoing subject of debate. Estimating these determinants becomes a valuable tool for predicting price trends and mitigating the risks of market volatility. This article presents a systematic review analyzing studies that compare various machine learning (ML) tools with hedonic regression, aiming to assess whether real estate price predictions based on mathematical techniques and artificial intelligence enhance the accuracy of hedonic price models used for valuing residential properties. ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. However, hedonic regression models, while less precise, are more robust, as they can identify the housing attributes that most influence price levels. These attributes include the property\u2019s location, its internal features, and the distance from the property to city centers.</jats:p>",47 "DOI": "10.3390/urbansci9020032",48 "type": "journal-article",49 "created": {50 "date-parts": [51 [52 2025,53 1,54 3155 ]56 ],57 "date-time": "2025-01-31T05:08:26Z",58 "timestamp": 173830010600059 },60 "page": "32",61 "update-policy": "https://doi.org/10.3390/mdpi_crossmark_policy",62 "source": "Crossref",63 "is-referenced-by-count": 18,64 "title": [65 "Comparative Analysis of Advanced Models for Predicting Housing Prices: A Review"66 ],67 "prefix": "10.3390",68 "volume": "9",69 "author": [70 {71 "ORCID": "https://orcid.org/0009-0008-8600-6417",72 "authenticated-orcid": false,73 "given": "Inmaculada",74 "family": "Moreno-Foronda",75 "sequence": "first",76 "affiliation": [77 {78 "name": "Department of Applied Economics, University of Granada, 18011 Granada, Spain"79 }80 ],81 "role": [82 {83 "vocabulary": "crossref",84 "role": "author"85 }86 ]87 },88 {89 "ORCID": "https://orcid.org/0000-0002-7864-2025",90 "authenticated-orcid": false,91 "given": "Mar\u00eda-Teresa",92 "family": "S\u00e1nchez-Mart\u00ednez",93 "sequence": "additional",94 "affiliation": [95 {96 "name": "Department of Applied Economics, University of Granada, 18011 Granada, Spain"97 }98 ],99 "role": [100 {101 "vocabulary": "crossref",102 "role": "author"103 }104 ]105 },106 {107 "ORCID": "https://orcid.org/0000-0002-4031-0949",108 "authenticated-orcid": false,109 "given": "Montserrat",110 "family": "Pareja-Eastaway",111 "sequence": "additional",112 "affiliation": [113 {114 "name": "Department of Economics, University of Barcelona, 08034 Barcelona, Spain"115 }116 ],117 "role": [118 {119 "vocabulary": "crossref",120 "role": "author"121 }122 ]123 }124 ],125 "member": "1968",126 "published-online": {127 "date-parts": [128 [129 2025,130 1,131 31132 ]133 ]134 },135 "reference": [136 {137 "key": "ref_1",138 "first-page": "221",139 "article-title": "Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea",140 "volume": "36",141 "author": "Mark",142 "year": "2007",143 "journal-title": "Aust. Geogr."144 },145 {146 "key": "ref_2",147 "doi-asserted-by": "crossref",148 "first-page": "288",149 "DOI": "10.1002/for.2907",150 "article-title": "The effect of environment on housing prices: Evidence from the Google Street View",151 "volume": "42",152 "author": "Wang",153 "year": "2023",154 "journal-title": "J. Forecast."155 },156 {157 "key": "ref_3",158 "doi-asserted-by": "crossref",159 "first-page": "18",160 "DOI": "10.1177/0885412210386540",161 "article-title": "Hedonic price effects of pedestrian- and transit-oriented development",162 "volume": "26",163 "author": "Bartholomew",164 "year": "2011",165 "journal-title": "J. Plan. Lit."166 },167 {168 "key": "ref_4",169 "doi-asserted-by": "crossref",170 "first-page": "312",171 "DOI": "10.1016/j.red.2022.10.002",172 "article-title": "Regional divergence and house prices",173 "volume": "49",174 "author": "Howard",175 "year": "2023",176 "journal-title": "Rev. Econ. Dyn."177 },178 {179 "key": "ref_5",180 "doi-asserted-by": "crossref",181 "first-page": "357",182 "DOI": "10.1108/IJHMA-04-2019-0044",183 "article-title": "Bibliometric analysis of hedonic price model using CiteSpace",184 "volume": "13",185 "author": "Jayantha",186 "year": "2019",187 "journal-title": "Int. J. Hous. Mark. Anal."188 },189 {190 "key": "ref_6",191 "doi-asserted-by": "crossref",192 "unstructured": "Xiao, Y. (2017). Urban Morphology and Housing Market, Springer.",193 "DOI": "10.1007/978-981-10-2762-8"194 },195 {196 "key": "ref_7",197 "unstructured": "Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing, Long Beach, CA, USA."198 },199 {200 "key": "ref_8",201 "doi-asserted-by": "crossref",202 "unstructured": "Fan, C., Cui, Z., and Zhong, X. (2018, January 26\u201328). House prices prediction with machine learning algorithms. Proceedings of the ICMLC\u201918: 2018 10th International Conference on Machine Learning and Computing, Macau, China.",203 "DOI": "10.1145/3195106.3195133"204 },205 {206 "key": "ref_9",207 "unstructured": "Rouhiaine, L. (2018). Inteligencia Artificial: 101 Cosas Que Debes Saber Hoy Sobre Nuestro Future, Editorial Planeta S.A."208 },209 {210 "key": "ref_10",211 "doi-asserted-by": "crossref",212 "first-page": "1797",213 "DOI": "10.1007/s00477-016-1213-y",214 "article-title": "Application of wavelet-artificial intelligence hybrid models for water quality prediction: A case study in Aji-Chai River, Iran",215 "volume": "30",216 "author": "Barzegar",217 "year": "2016",218 "journal-title": "Stoch. Environ. Res. Risk Assess."219 },220 {221 "key": "ref_11",222 "doi-asserted-by": "crossref",223 "first-page": "114",224 "DOI": "10.1111/ecin.12364",225 "article-title": "Big data and big cities: The promises and limitations of improved measures of urban life",226 "volume": "56",227 "author": "Glaeser",228 "year": "2016",229 "journal-title": "Econ. Inq."230 },231 {232 "key": "ref_12",233 "doi-asserted-by": "crossref",234 "first-page": "114590",235 "DOI": "10.1016/j.eswa.2021.114590",236 "article-title": "Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain",237 "volume": "171",238 "year": "2021",239 "journal-title": "Expert Syst. Appl."240 },241 {242 "key": "ref_13",243 "doi-asserted-by": "crossref",244 "first-page": "291",245 "DOI": "10.1006/juec.1997.2071",246 "article-title": "Andrew Court and the invention of hedonic price analysis",247 "volume": "44",248 "author": "Goodman",249 "year": "1998",250 "journal-title": "J. Urban Econ."251 },252 {253 "key": "ref_14",254 "doi-asserted-by": "crossref",255 "first-page": "34",256 "DOI": "10.1086/260169",257 "article-title": "Hedonic prices and implicit markets: Product differentiation in pure competition",258 "volume": "82",259 "author": "Rosen",260 "year": "1974",261 "journal-title": "J. Political Econ."262 },263 {264 "key": "ref_15",265 "doi-asserted-by": "crossref",266 "first-page": "106",267 "DOI": "10.1080/07474938.2014.944791",268 "article-title": "Hedonic regressions and the decomposition of a house price index into land and structure components",269 "volume": "34",270 "author": "Diewert",271 "year": "2011",272 "journal-title": "Econom. Rev."273 },274 {275 "key": "ref_16",276 "doi-asserted-by": "crossref",277 "first-page": "425",278 "DOI": "10.1111/j.1467-9787.2006.00480.x",279 "article-title": "Economic fundamentals in local housing markets: Evidence from U.S. metropolitan regions",280 "volume": "46",281 "author": "Hwang",282 "year": "2006",283 "journal-title": "J. Reg. Sci."284 },285 {286 "key": "ref_17",287 "unstructured": "Mendoza, N.J. (2009). La Red de Hopfield. Rev. Inf. Tecnol. Y Soc. , 35."288 },289 {290 "key": "ref_18",291 "doi-asserted-by": "crossref",292 "unstructured": "Shinde, P.P., and Shah, S. (2018, January 16\u201318). A review of machine learning and deep learning applications. Proceedings of the International Fourth Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.",293 "DOI": "10.1109/ICCUBEA.2018.8697857"294 },295 {296 "key": "ref_19",297 "doi-asserted-by": "crossref",298 "first-page": "273",299 "DOI": "10.1007/BF00994018",300 "article-title": "Support-vector networks",301 "volume": "20",302 "author": "Cortes",303 "year": "1995",304 "journal-title": "Mach. Learn."305 },306 {307 "key": "ref_20",308 "doi-asserted-by": "crossref",309 "unstructured": "Angra, S., and Ahuja, S. (2017, January 23\u201325). Machine learning and its application: A review. Proceedings of the International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, India.",310 "DOI": "10.1109/ICBDACI.2017.8070809"311 },312 {313 "key": "ref_21",314 "doi-asserted-by": "crossref",315 "first-page": "2201",316 "DOI": "10.1007/s00521-023-08823-3",317 "article-title": "Exploration of intelligent housing price forecasting based on the anchoring effect",318 "volume": "36",319 "author": "Song",320 "year": "2024",321 "journal-title": "Neural Comput. Appl."322 },323 {324 "key": "ref_22",325 "first-page": "9",326 "article-title": "Investigaci\u00f3n documental, investigaci\u00f3n bibliom\u00e9trica y revisiones sistem\u00e1ticas",327 "volume": "31",328 "year": "2023",329 "journal-title": "Rev. Electr\u00f3nica De Humanidades Educ. Y Comun. Soc."330 },331 {332 "key": "ref_23",333 "doi-asserted-by": "crossref",334 "first-page": "149",335 "DOI": "10.1016/j.ciresp.2011.07.009",336 "article-title": "Revisiones sistem\u00e1ticas de la literatura. Qu\u00e9 se debe saber acerca de ellas",337 "volume": "91",338 "author": "Manterola",339 "year": "2013",340 "journal-title": "Cirug\u00eda Espa\u00f1ola"341 },342 {343 "key": "ref_24",344 "doi-asserted-by": "crossref",345 "first-page": "790",346 "DOI": "10.1016/j.recesp.2021.06.016",347 "article-title": "Declaraci\u00f3n PRISMA 2020: Una gu\u00eda actualizada para la publicaci\u00f3n de revisiones sistem\u00e1ticas",348 "volume": "74",349 "author": "Page",350 "year": "2021",351 "journal-title": "Rev. Espa\u00f1ola De Cardiol."352 },353 {354 "key": "ref_25",355 "first-page": "60",356 "article-title": "Revisiones sistem\u00e1ticas de la literatura",357 "volume": "20",358 "year": "2005",359 "journal-title": "Rev. Colomb. De Gastroenterol."360 },361 {362 "key": "ref_26",363 "doi-asserted-by": "crossref",364 "first-page": "117",365 "DOI": "10.1186/s13643-021-01671-z",366 "article-title": "How to properly use the PRISMA statement",367 "volume": "10",368 "author": "Aromataris",369 "year": "2021",370 "journal-title": "Syst. Rev."371 },372 {373 "key": "ref_27",374 "doi-asserted-by": "crossref",375 "first-page": "2079",376 "DOI": "10.1007/s10901-022-09937-1",377 "article-title": "Comparison of machine learning models to provide preliminary forecasts of real estate prices",378 "volume": "37",379 "author": "Chou",380 "year": "2022",381 "journal-title": "J. Hous. Built Environ."382 },383 {384 "key": "ref_28",385 "doi-asserted-by": "crossref",386 "first-page": "31",387 "DOI": "10.17849/insm-47-01-31-39.1",388 "article-title": "Random forest",389 "volume": "47",390 "author": "Rigatti",391 "year": "2017",392 "journal-title": "J. Insur. Med."393 },394 {395 "key": "ref_29",396 "doi-asserted-by": "crossref",397 "first-page": "289",398 "DOI": "10.1214/10-STS330",399 "article-title": "To explain or to predict?",400 "volume": "25",401 "author": "Shmueli",402 "year": "2010",403 "journal-title": "Stat. Sci."404 },405 {406 "key": "ref_30",407 "first-page": "41",408 "article-title": "Obtenci\u00f3n de precios impl\u00edcitos para atributos determinantes en la valoraci\u00f3n de una vivienda",409 "volume": "5",410 "author": "Tabales",411 "year": "2012",412 "journal-title": "Rev. Int. De Adm. Y Finanz."413 },414 {415 "key": "ref_31",416 "doi-asserted-by": "crossref",417 "first-page": "104486",418 "DOI": "10.1016/j.landurbplan.2022.104486",419 "article-title": "Assessing the value of user-generated images of urban surroundings for house price estimation",420 "volume": "226",421 "author": "Chen",422 "year": "2022",423 "journal-title": "Landsc. Urban Plan."424 },425 {426 "key": "ref_32",427 "first-page": "1",428 "article-title": "A house price valuation based on the random forest approach: The mass appraisal of residential property in South Korea",429 "volume": "24",430 "author": "Hong",431 "year": "2020",432 "journal-title": "Int. J. Strateg. Prop. Manag."433 },434 {435 "key": "ref_33",436 "doi-asserted-by": "crossref",437 "first-page": "273",438 "DOI": "10.3846/1648715X.2016.1259190",439 "article-title": "Forecasting spatial dynamics of the housing market using Support Vector Machine",440 "volume": "21",441 "author": "Chen",442 "year": "2017",443 "journal-title": "Int. J. Strateg. Prop. Manag."444 },445 {446 "key": "ref_34",447 "doi-asserted-by": "crossref",448 "first-page": "2843",449 "DOI": "10.1016/j.eswa.2008.01.044",450 "article-title": "Determinants of house prices in Turkey: Hedonic regression versus artificial neural network",451 "volume": "36",452 "author": "Selim",453 "year": "2009",454 "journal-title": "Expert Syst. Appl."455 },456 {457 "key": "ref_35",458 "doi-asserted-by": "crossref",459 "first-page": "293",460 "DOI": "10.1016/j.landurbplan.2012.06.009",461 "article-title": "Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY",462 "volume": "107",463 "author": "Yoo",464 "year": "2012",465 "journal-title": "Landsc. Urban Plan."466 },467 {468 "key": "ref_36",469 "doi-asserted-by": "crossref",470 "unstructured": "\u010ceh, M., Kilibarda, M., Lisec, A., and Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. Int. J. Geo-Inf., 7.",471 "DOI": "10.3390/ijgi7050168"472 },473 {474 "key": "ref_37",475 "doi-asserted-by": "crossref",476 "first-page": "24",477 "DOI": "10.1016/j.landurbplan.2015.04.007",478 "article-title": "Valuation of landscape amenities: A hedonic pricing analysis of housing rents in urban, suburban, and periurban Switzerland",479 "volume": "141",480 "author": "Waltert",481 "year": "2015",482 "journal-title": "Landsc. Urban Plan."483 },484 {485 "key": "ref_38",486 "doi-asserted-by": "crossref",487 "unstructured": "Wang, D., and Li, V.J. (2019). Mass appraisal models of real estate in the 21st century: A systematic literature review. Sustainability, 11.",488 "DOI": "10.3390/su11247006"489 },490 {491 "key": "ref_39",492 "doi-asserted-by": "crossref",493 "unstructured": "Ligus, M., and Peternek, P. (2017). Impacts of Urban Environmental Attributes on Residential Housing Price in Warsaw (Poland): Spatial Hedonic Analysis of City Districts, Springer.",494 "DOI": "10.1007/978-3-319-54885-2_15"495 },496 {497 "key": "ref_40",498 "doi-asserted-by": "crossref",499 "first-page": "664",500 "DOI": "10.1068/b38093",501 "article-title": "Local hedonic house-price modelling for urban planners: Advantages of using local regression techniques",502 "volume": "40",503 "author": "Crespo",504 "year": "2013",505 "journal-title": "Environ. Plan. B Plan. Des."506 },507 {508 "key": "ref_41",509 "doi-asserted-by": "crossref",510 "unstructured": "Afonso, B., Melo, L., Oliveira, W., Sousa, S., and Berton, L. (2019, January 15\u201318). Housing prices prediction with a deep learning and random forest ensemble. Proceedings of the Anais do Encontro Nacional de Intelig\u00eancia Artificial e Computacional (ENIAC), Salvador, Brasil.",511 "DOI": "10.5753/eniac.2019.9300"512 },513 {514 "key": "ref_42",515 "doi-asserted-by": "crossref",516 "unstructured": "Hoxha, V. (2024). Comparative analysis of machine learning models in predicting housing prices: A case study of Prishtina\u2019s real estate market. Int. J. Hous. Mark. Anal.",517 "DOI": "10.1108/IJHMA-09-2023-0120"518 },519 {520 "key": "ref_43",521 "doi-asserted-by": "crossref",522 "first-page": "48",523 "DOI": "10.1080/09599916.2020.1832558",524 "article-title": "Predicting property prices with machine learning algorithms",525 "volume": "38",526 "author": "Ho",527 "year": "2020",528 "journal-title": "J. Prop. Res."529 },530 {531 "key": "ref_44",532 "doi-asserted-by": "crossref",533 "first-page": "42",534 "DOI": "10.35940/ijitee.C9741.0111322",535 "article-title": "Housing price prediction with machine learning",536 "volume": "11",537 "author": "Begum",538 "year": "2022",539 "journal-title": "Int. J. Innov. Technol. Explor. Eng."540 },541 {542 "key": "ref_45",543 "first-page": "403",544 "article-title": "House type specification for housing development project using machine learning techniques: A study from Bangkok Metropolitan Region, Thailand",545 "volume": "23",546 "author": "Tochaiwat",547 "year": "2024",548 "journal-title": "J. Environ. Plan."549 },550 {551 "key": "ref_46",552 "doi-asserted-by": "crossref",553 "first-page": "806",554 "DOI": "10.1016/j.procs.2022.01.100",555 "article-title": "House price prediction using random forest machine learning technique",556 "volume": "199",557 "author": "Adetunji",558 "year": "2022",559 "journal-title": "Procedia Comput. Sci."560 },561 {562 "key": "ref_47",563 "doi-asserted-by": "crossref",564 "first-page": "103941",565 "DOI": "10.1016/j.cities.2022.103941",566 "article-title": "Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms",567 "volume": "131",568 "author": "Soltani",569 "year": "2022",570 "journal-title": "Cities"571 },572 {573 "key": "ref_48",574 "doi-asserted-by": "crossref",575 "first-page": "810",576 "DOI": "10.1016/j.trpro.2014.10.067",577 "article-title": "A neural network-based model for real estate price estimation considering environmental quality of property location",578 "volume": "3",579 "author": "Chiarazzo",580 "year": "2014",581 "journal-title": "Transp. Res. Procedia"582 },583 {584 "key": "ref_49",585 "doi-asserted-by": "crossref",586 "first-page": "567",587 "DOI": "10.1111/saje.12047",588 "article-title": "A flexible neuro-fuzzy approach for improvement of seasonal housing price estimation in uncertain and non-linear environments",589 "volume": "82",590 "author": "Azadeh",591 "year": "2014",592 "journal-title": "South Afr. J. Econ."593 },594 {595 "key": "ref_50",596 "doi-asserted-by": "crossref",597 "unstructured": "Chaturvedi, V., and de Vries, W.T. (2021). Machine learning algorithms for urban land use planning: A review. Urban Sci., 5.",598 "DOI": "10.3390/urbansci5030068"599 },600 {601 "key": "ref_51",602 "doi-asserted-by": "crossref",603 "first-page": "104432",604 "DOI": "10.1016/j.cities.2023.104432",605 "article-title": "Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit and density factors on single-family housing price",606 "volume": "140",607 "author": "Chen",608 "year": "2023",609 "journal-title": "Cities"610 },611 {612 "key": "ref_52",613 "doi-asserted-by": "crossref",614 "first-page": "100166",615 "DOI": "10.1016/j.dajour.2023.100166",616 "article-title": "A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization",617 "volume": "6",618 "author": "Lahmiri",619 "year": "2023",620 "journal-title": "Decis. Anal. J."621 },622 {623 "key": "ref_53",624 "doi-asserted-by": "crossref",625 "unstructured": "Paik, J., Baek, S.-J., Kim, J.-W., and Ko, K. (2023). Influence of social overhead capital facilities on housing prices using machine learning. Appl. Sci., 13.",626 "DOI": "10.3390/app131910732"627 },628 {629 "key": "ref_54",630 "doi-asserted-by": "crossref",631 "first-page": "11829",632 "DOI": "10.1007/s00500-018-03739-w",633 "article-title": "Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm",634 "volume": "23",635 "author": "Rui",636 "year": "2019",637 "journal-title": "Soft Comput."638 },639 {640 "key": "ref_55",641 "doi-asserted-by": "crossref",642 "unstructured": "Kou, J., Du, J., Fu, X., Zhang, G.Z., Wang, H., and Zhang, Y. (2021). The Effect of Regional Economic Clusters on Housing Price, Springer. Lecture Notes in Artificial Intelligence.",643 "DOI": "10.1007/978-3-030-69377-0_15"644 },645 {646 "key": "ref_56",647 "doi-asserted-by": "crossref",648 "first-page": "102409",649 "DOI": "10.1016/j.habitatint.2021.102463",650 "article-title": "Google Maps amenities and condominium prices: Investigating the effects and relationships using machine learning",651 "volume": "118",652 "author": "Taecharungroj",653 "year": "2021",654 "journal-title": "Habitat Int."655 },656 {657 "key": "ref_57",658 "doi-asserted-by": "crossref",659 "first-page": "102409",660 "DOI": "10.1016/j.habitatint.2022.102660",661 "article-title": "An explainable model for the mass appraisal of residences: The application of tree-based machine learning algorithms and interpretation of value determinants",662 "volume": "128",663 "author": "Iban",664 "year": "2022",665 "journal-title": "Habitat Int."666 }667 ],668 "container-title": [669 "Urban Science"670 ],671 "original-title": [],672 "language": "en",673 "link": [674 {675 "URL": "https://www.mdpi.com/2413-8851/9/2/32/pdf",676 "content-type": "unspecified",677 "content-version": "vor",678 "intended-application": "similarity-checking"679 }680 ],681 "deposited": {682 "date-parts": [683 [684 2025,685 10,686 9687 ]688 ],689 "date-time": "2025-10-09T16:24:44Z",690 "timestamp": 1760027084000691 },692 "score": 1,693 "resource": {694 "primary": {695 "URL": "https://www.mdpi.com/2413-8851/9/2/32"696 }697 },698 "subtitle": [],699 "short-title": [],700 "issued": {701 "date-parts": [702 [703 2025,704 1,705 31706 ]707 ]708 },709 "references-count": 57,710 "journal-issue": {711 "issue": "2",712 "published-online": {713 "date-parts": [714 [715 2025,716 2717 ]718 ]719 }720 },721 "alternative-id": [722 "urbansci9020032"723 ],724 "URL": "https://doi.org/10.3390/urbansci9020032",725 "relation": {},726 "ISSN": [727 "2413-8851"728 ],729 "issn-type": [730 {731 "value": "2413-8851",732 "type": "electronic"733 }734 ],735 "subject": [],736 "published": {737 "date-parts": [738 [739 2025,740 1,741 31742 ]743 ]744 }745 }746}