Texas rural land market integration: A causal analysis using machine learning applications
Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% o...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682702400080X |
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| author | Tian Su Senarath Dharmasena David Leatham Charles Gilliland |
| author_facet | Tian Su Senarath Dharmasena David Leatham Charles Gilliland |
| author_sort | Tian Su |
| collection | DOAJ |
| description | Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas’ diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation. |
| format | Article |
| id | doaj-art-c38d109f679a4139947c84e4bf41daa4 |
| institution | OA Journals |
| issn | 2666-8270 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-c38d109f679a4139947c84e4bf41daa42025-08-20T01:59:09ZengElsevierMachine Learning with Applications2666-82702024-12-011810060410.1016/j.mlwa.2024.100604Texas rural land market integration: A causal analysis using machine learning applicationsTian Su0Senarath Dharmasena1David Leatham2Charles Gilliland3Texas Real Estate Research Center, 2115 TAMU, Texas A&M University, College Station 77843-2115, TX, USADepartment of Agricultural Economics, 2124 TAMU, Texas A&M University, College Station 77843-2124, TX, USA; Corresponding author.Department of Agricultural Economics, 2124 TAMU, Texas A&M University, College Station 77843-2124, TX, USATexas Real Estate Research Center, 2115 TAMU, Texas A&M University, College Station 77843-2115, TX, USATexas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas’ diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation.http://www.sciencedirect.com/science/article/pii/S266682702400080XTexas rural land marketsMachine learningProbabilistic graphical modelDirected acyclic graphs |
| spellingShingle | Tian Su Senarath Dharmasena David Leatham Charles Gilliland Texas rural land market integration: A causal analysis using machine learning applications Machine Learning with Applications Texas rural land markets Machine learning Probabilistic graphical model Directed acyclic graphs |
| title | Texas rural land market integration: A causal analysis using machine learning applications |
| title_full | Texas rural land market integration: A causal analysis using machine learning applications |
| title_fullStr | Texas rural land market integration: A causal analysis using machine learning applications |
| title_full_unstemmed | Texas rural land market integration: A causal analysis using machine learning applications |
| title_short | Texas rural land market integration: A causal analysis using machine learning applications |
| title_sort | texas rural land market integration a causal analysis using machine learning applications |
| topic | Texas rural land markets Machine learning Probabilistic graphical model Directed acyclic graphs |
| url | http://www.sciencedirect.com/science/article/pii/S266682702400080X |
| work_keys_str_mv | AT tiansu texasrurallandmarketintegrationacausalanalysisusingmachinelearningapplications AT senarathdharmasena texasrurallandmarketintegrationacausalanalysisusingmachinelearningapplications AT davidleatham texasrurallandmarketintegrationacausalanalysisusingmachinelearningapplications AT charlesgilliland texasrurallandmarketintegrationacausalanalysisusingmachinelearningapplications |