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 1112 ]13 ],14 "date-time": "2026-09-11T16:13:20Z",15 "timestamp": 1789143200269,16 "version": "build-2803163510"17 },18 "reference-count": 0,19 "publisher": "Malaysian Institute of Planners",20 "license": [21 {22 "start": {23 "date-parts": [24 [25 2021,26 10,27 1728 ]29 ],30 "date-time": "2021-10-17T00:00:00Z",31 "timestamp": 163442880000032 },33 "content-version": "unspecified",34 "delay-in-days": 0,35 "URL": "https://creativecommons.org/licenses/by-nc-nd/3.0/"36 }37 ],38 "content-domain": {39 "domain": [],40 "crossmark-restriction": false41 },42 "short-container-title": [43 "PM"44 ],45 "abstract": "<jats:p>Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for price prediction and valuation. Authors seek to explore optimal solutions in predicting the property price indices, that will be beneficial to the policymakers in assessing the overall economic situation. This study also looks into the use of machine learning in property valuation towards identifying the best model in predicting property values based on its characteristics such as location, land size, number of rooms and others. A systematic review was conducted to review previous studies that imposed machine learning as statistical tool in predicting and valuing property prices. Articles on real estate price prediction and price valuation using machine learning techniques were observed using electronics database. The finding shows the increasing use of this method in the real estate field. The most successful machine learning algorithms used is the Random Forest that has better compatibility to the data situation.</jats:p>",46 "DOI": "10.21837/pm.v19i17.1018",47 "type": "journal-article",48 "created": {49 "date-parts": [50 [51 2021,52 10,53 1754 ]55 ],56 "date-time": "2021-10-17T22:26:28Z",57 "timestamp": 163450958800058 },59 "source": "Crossref",60 "is-referenced-by-count": 25,61 "title": [62 "MACHINE LEARNING FOR PROPERTY PRICE PREDICTION AND PRICE VALUATION: A SYSTEMATIC LITERATURE REVIEW"63 ],64 "prefix": "10.21837",65 "volume": "19",66 "author": [67 {68 "given": "Nur Shahirah",69 "family": "Ja\u2019afar",70 "sequence": "first",71 "affiliation": [],72 "role": [73 {74 "vocabulary": "crossref",75 "role": "author"76 }77 ]78 },79 {80 "given": "Junainah",81 "family": "Mohamad",82 "sequence": "additional",83 "affiliation": [],84 "role": [85 {86 "vocabulary": "crossref",87 "role": "author"88 }89 ]90 },91 {92 "given": "Suriatini",93 "family": "Ismail",94 "sequence": "additional",95 "affiliation": [],96 "role": [97 {98 "vocabulary": "crossref",99 "role": "author"100 }101 ]102 }103 ],104 "member": "9297",105 "published-online": {106 "date-parts": [107 [108 2021,109 10,110 17111 ]112 ]113 },114 "container-title": [115 "PLANNING MALAYSIA"116 ],117 "original-title": [],118 "link": [119 {120 "URL": "https://journals.iium.edu.my/pm/ojs324/index.php/pmj/article/download/1018/714",121 "content-type": "application/pdf",122 "content-version": "vor",123 "intended-application": "text-mining"124 },125 {126 "URL": "https://journals.iium.edu.my/pm/ojs324/index.php/pmj/article/download/1018/714",127 "content-type": "unspecified",128 "content-version": "vor",129 "intended-application": "similarity-checking"130 }131 ],132 "deposited": {133 "date-parts": [134 [135 2022,136 2,137 9138 ]139 ],140 "date-time": "2022-02-09T03:11:01Z",141 "timestamp": 1644376261000142 },143 "score": 1,144 "resource": {145 "primary": {146 "URL": "https://journals.iium.edu.my/pm/ojs324/index.php/pmj/article/view/1018"147 }148 },149 "subtitle": [],150 "short-title": [],151 "issued": {152 "date-parts": [153 [154 2021,155 10,156 17157 ]158 ]159 },160 "references-count": 0,161 "URL": "https://doi.org/10.21837/pm.v19i17.1018",162 "relation": {},163 "ISSN": [164 "0128-0945",165 "1675-6215"166 ],167 "issn-type": [168 {169 "value": "0128-0945",170 "type": "electronic"171 },172 {173 "value": "1675-6215",174 "type": "print"175 }176 ],177 "subject": [],178 "published": {179 "date-parts": [180 [181 2021,182 10,183 17184 ]185 ]186 }187 }188}