Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 9568-9578 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: baolaw@yahoo.com Applying economic theories to build land price model and develop land bank: A case study of Vinh Long, Viet Nam Le Tran Bao1*, Nguyen Ngoc Duy Phuong2 1,2International University, Vietnam National University Ho Chi Minh City, Vietnam Postal address: Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City; baolaw@yahoo.com (L.T.B.). Abstract: The paper explores the relationships between land price and land bank in the well – known philosophy of the urban economic theory and the bid rent theory. Based on those, the article aims to determine the impact of factors on land prices and propose some recommendations for creating a land bank to implement investment projects in Vinh Long province. Secondary data was collected from agencies and functional units in Vinh Long province. Primary data was collected through a survey of 144 knowledgeable people related to state management of land, land prices, and land bank creation. The data were analyzed through Cronbach's Alpha coefficient, factor analysis, and testing of the SEM linear structural model using Smart PLS software. There are 36 measured factors belonging to 7 groups of factors included in the regression model, of which five groups of factors impact land prices and are intermediate factors affecting the creation of land banks. When determining land prices, it is necessary to pay attention to the level of impact of groups of factors on residential land prices, such as supply and demand, land plot location, urbanization, and so on, to determine appropriate land prices in each situation. The stage of the urbanization process ensures harmony between the state's interests, land users, and other subjects related to creating a land bank in Vinh Long province. Keywords: Economy, Land bank, Land price, Legal factor, Urbanization. 1. Introduction Urbanization in Vietnam is in a period of solid development, causing the need for land to build housing projects, transportation systems, commercial centers, industrial parks, and more to increase. Still, the land bank is limited, which invisibly causes land prices to increase. In addition, there is land speculation and brokers for personal gain have provided fake information to push land prices higher and higher. Therefore, it is difficult for low-income people to access this scarce resource. According to Vu & Kawashima (2018), land prices in major Vietnamese cities are on par with land prices in residential areas in some locations in the center of Tokyo. However, the Japanese capital and Vietnam have a big way to go. The problem is accurately determining the land price according to its value. Many researchers have been carrying out research to determine factors affecting land prices for a long time. Most research results show that location and area are the two factors that strongly influence land prices (Northcraft & Small, 1979). Han et al. (2011) presented research results on joint economic, political, social, population, and global factors. Regional factors include traffic conditions, environment, prosperity (GDP), population density and location. Individual factors such as land plot shape, width, depth, and area affect land prices in Tsingtao, China.Sklenicka et al. (2013) studied the factors: Proximity to a Settlement, Municipality Population, Travel Time to the Capital City, The accessibility of a parcel, Natural Soil Fertility, Parcel Size, and Speculative purchases all affect land prices in Agriculture in the Czech Republic. Phan & Phan (2021) presented research results identifying six factors affecting land prices in Can Tho City. Factors include natural (geographical), economic, infrastructure, legal, state policies, and social factors, which positively correlate with land prices. Ho,. et al. (2020) studied factors affecting https://orcid.org/0000-0001-6695-7022 9569 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate residential land prices in the cities of Chi Linh and Hai Duong, including infrastructure, individual factors (plot shape, width, depth, area), economic, location, legal, and social factors. The results show that these six factors explain 68.1% of land price fluctuations. Vinh Long province is located adjacent to the city. Can Tho, considered a vital transportation bridge between Ho Chi Minh and the Mekong Delta provinces and international transportation, plays a strong driving force in promoting the development of the entire Mekong Delta region. In recent years, land prices in the area have also increased proportionally to local development, and there is a significant difference between land prices regulated by the State and land prices on the market. This gap is different in some areas up to 7-8 times. This study aims to identify factors affecting land prices in Vinh Long based on factor analysis, test the SEM linear structural model using Smart PLS software, and propose central implication policies for creating land banks for local investment projects. 2. Materials and Methods 2.1. Hypothesis According to El-Barmelgy,. et al (2014), the urban economic theory that mainly focus on land. In economic terms, land is a complex object endowed with dual characteristics. First, land is a commodity in the usual economic sense. Second, unlike other commodities, land is completely immobile. Hence, each piece of lands is associated with a unique location in geographic space. These dual characteristics of a land induce strong no convexity in consumers’ preferences. The spatial characteristics, the externalities, and intervention make an analysis for the land market rather complicated. The bid rent theory is considered as microeconomic theory and was basically formed in the context of urban land uses and urban land values. The term bid rent refers to the amount of rent a user is willing to pay for a more central location, but is willing to accept a location further from the central built-up area at a lower rent cost in compensation (L. Narvaez, et al., 2013). This output in a convex land price curve with the highest land prices close to the city centre. Based on the results of studies by authors Northcraft & Small (1979), Reynolds & Tower (1978) in the US, Han et al. (2011) in China, and Phan & Phan (2021) in Can ThoCity and some other authors mentioned above, as well as the characteristics of the research location built the research model (Figure 1). All groups of natural factors, groups of urbanization factors, groups of economic factors, groups of social factors, and groups of legal factors are assumed to influence land prices to form land banks in localities positively Vinh Long. Figure 1. Research models. 2.2. Data Collection Secondary data sources: Using data from published domestic and foreign research (data from foreign 9570 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate and domestic magazines) and statistical reports from state-run agencies. Regarding natural resources and the environment, from surveys and data analysis, students can draw out factors affecting the land bank of Vinh Long province. Primary data source: Random sampling was done by sending survey questionnaires to 144 officials, civil servants, lecturers in the natural resources and environment sector, and people's committees from provinces to districts and communes using the Google Forms tool. According to Nguyen (2014), the minimum sample size is n ≥ 50 + 8*p, where p is the number of independent variables. So, the minimum sample size is n ≥ 8*5 + 50 <=> n ≥ 90. In this research article, the author surveyed all civil servants, public employees, and land researchers, 144 people. Use a 5-level Likert scale with the following corresponding levels: 1 = completely disagree; 2 = disagree; 3 = neutral; 4 = agree, and 5 = agree entirely. After being adjusted to suit local practice, the scale measuring land bank factors results include 6 hidden factors with 31 observed variables used as the primary scale for the study. Six hidden factors (cause scale) that impact the Land Bank factor include (1) Natural factors, (2) Urbanization factors, (3) Economic factors, (4) Social factors that affect "Land price" impact the variable "Landbank" (result scale): Summary of scale observed variables and variable symbols as follows: 2.2.1. Land Price (LP) Variable Winning auction price (LP1), Exchange price (LP2), Transfer price (LP3), State-regulated price (LP4) and price determined by judicial authorities (LP5) 2.2.2. Land Bank Variables (LB) Land recovery (LB1), Receive transfer (LB2), Encroachment on rivers, canals, mudflats (LB3), Voluntary return of land (LB4), and State recovery of land due to violations of law Land law (LB5) 2.2.3. Variables of Natural Factors of the Land Plot (NF) Land plot adjacent to the road (NF1), Land plot at the intersection (NF2), Area and size of land plot (NF3), Soil (NF4), Near the urban center (NF5). 2.2.4. Urbanization Variable (UB) Level of infrastructure investment (UB1), rate of immigration (UB2), level of environmental pollution (UB3), and level of investment in project construction (UB4) 2.2.5. Economic Factor Variables (EF) Bank interest rate (EF1), Average income per capita (EF2), Land speculation (EF3), Land purchase and sale demand (EF4), investment Implement a business project (EF5) 2.2.6. Social Factor Variables (SF) Population size (SF1), Employment rate (SF2), Security and order (SF3), Utilities services (SF4) and Intellectual level ( SF5) 2.2.7. Legal Factor Variables (LF) Planned land plot (LF1), Land plot allowed to change purpose (LF2), State land price determination (LF3), Tax and registration policies (LF4), Land use term (LF5), Land plot with legal documents (LF6), Regulations on land division (LF7) 2.3. Data Analysis The Smart PLS diagram model is represented as follows (Figure 2). 9571 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate Figure 2. Smart PLS diagram model. To evaluate the measurement model on SMART PLS, we evaluate on the following criteria: 1. Quality of observed variables of factors: Outer loading has a good meaning of 0.7 or higher. Reliability Cronbach's alpha scale reliability ≥ 0.7 (DeVellis, 2012), Composite reliability ≥ 0.7(Hair et al., 2014) 2 . AVE convergence (convergence) ≥ 0.5 (Hock & Ringle, 2010) 3. Discriminant Validity: • Using the Fornell and Larcker table, discrimination will be guaranteed when the square root of the AVE index of a factor is more significant than all correlation coefficients of that factor with other factors in the model. • Equal to HTMT table ≤ 0. 85 After evaluating the measurement model on SMARTPLS, we will continue to evaluate the structural model (SEM) based on considering the following factors: 1. Calculate the collinearity of independent variables with inner VIF < 5 2. Meaning of impact relationships in the model (Path Coefficients): P value < 0.05 is a statistically significant relationship, and P value > 0.05 is statistically insignificant. The original sample path coefficient has a positive sign for a positive effect (+) and a negative sign for a negative effect (-). 3. Evaluate the coefficient of determination R square (R square) R-squared approaching 1 means the level of explanation for the dependent variable is high, and approaching 0 means the level of explanation for the dependent variable is low. (There is no R-squared threshold for how much is passed or what is not). (Hair et al., 2017) 4. Evaluate the impact coefficient f square (f square) • f square < 0.02: the impact level is small or insignificant. • 0.02 ≤ f square < 0.15: small impact level. • f square < 0.35: average impact level. 9572 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate • f square ≥ 0.35: large impact level (Cohen, 1988) 3. Results and Discussion 3.1. Measurement Model Test Results Assessing the quality of observed variables of the factors through PLS-SEM algorithm analysis, showing the results of the outer loading coefficients of the variables from 0.537 to 0.9, of which 7 variables are in the range of 0.5 to 0.5. below 0.7, including EF4 = 0.691, LB5 = 0.687, LF4 = 0.681, LF6 = 0.672, LF7 = 0.645, SF1 = 0.682, and SF2 = 0.537, the corresponding Cronbach's Alpha, CR, and AVE indexes all meet the requirements, so the study decided to retain these observed variables to ensure the content validity of the scale. Therefore, it is concluded that the observed variables in the model all have convergent values. Table 1. Outer loading. EF LB LF LP NF SF UB LF x LP EF1 0.881 EF2 0.822 EF3 0.883 EF4 0.691 EF5 0.795 LB1 0.797 LB2 0.846 LB3 0.858 LB4 0.860 LB5 0.687 LF1 0.812 LF2 0.717 LF3 0.749 LF4 0.681 LF5 0.769 LF6 0.672 LF7 0.645 LP1 0.805 LP2 0.743 LP3 0.860 LP4 0.721 LP5 0.900 NF1 0.817 NF2 0.777 NF3 0.808 NF4 0.807 NF5 0.847 SF1 0.682 SF2 0.537 SF3 0.758 SF4 0.848 SF5 0.851 UB1 0.873 UB2 0.913 UB3 0.875 UB4 0.803 9573 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate LFxLP 1,000 Table 2. Scale reliability of Cronbach's Alpha, CR, AVE. Cronbach'salpha Compositereliabilit y (rho_a) Compositereliabilit y (rho_c) Averagevarianceextra cted (AVE) EF 0.874 0.888 0.909 0.668 LB 0.869 0.883 0.906 0.659 LF 0.847 0.853 0.884 0.522 LP 0.866 0.872 0.904 0.654 NF 0.870 0.871 0.906 0.658 SF 0.817 0.859 0.858 0.554 UB 0.889 0.891 0.924 0.752 The results show that all factor structures are reliable when Cronbach's alpha and Composite reliability coefficients (rho_c) aremore significant than 0.7, and the AVE indexes are all from 0.5. All factors ensure convergence Table 3. Discriminant validity. Fornell – Larker Criterion EF LB LF LP NF SF UB EF 0.818 LB 0.342 0.812 LF 0.599 0.412 0.723 LP 0.737 0.514 0.638 0.809 NF 0.708 0.491 0.518 0.707 0.811 SF 0.384 0.309 0.309 0.409 0.245 0.745 UB 0.523 0.589 0.448 0.708 0.530 0.358 0.867 Heterotrait – Monotrait Ratio (HTMT) (<0.85) EF LB 0.388 LF 0.692 0.467 LP 0.842 0.577 0.742 NF 0.812 0.563 0.600 0.810 SF 0.403 0.315 0.341 0.397 0.247 UB 0.588 0.666 0.511 0.796 0.592 0.369 LF x LP 0.600 0.113 0.593 0.545 0.543 0.183 0.449 9574 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate 3.2. Structural Model Testing Results Figure 3 showing the SEM structural model. Figure 3. SEM structural model. Table 4. VIF value. VIF EF -> LP 2.545 LF -> LB 1.888 LF -> LP 1.658 LP -> LB 1.799 NF -> LP 2.218 SF -> LP 1.243 UB -> LP 1.586 LF x LP -> LB 1.504 The VIF coefficient results are all less than 3, showing no multi collinearity phenomenon between the independent and dependent variables. 9575 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate Table 5. Results of testing path coefficients. Originalsample (O) Samplemean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values EF -> LP 0.253 0.245 0.092 2,738 0.006 LF -> LB 0.253 0.260 0.185 1,367 0.172 LF -> LP 0.192 0.207 0.097 1,987 0.047 LP -> LB 0.506 0.478 0.188 2,694 0.007 NF -> LP 0.229 0.228 0.074 3,093 0.002 SF -> LP 0.074 0.078 0.065 1,132 0.258 UB -> LP 0.342 0.332 0.069 4,942 0.000 LF x LP -> LB 0.140 0.132 0.057 2,439 0.015 Note: P value of SF -> LP = 0.258 > 0.05, so it is not statistically significant. Except for the variable SF, all the impact coefficients of the independent variables in the Original Sample column have positive signs. Hence, the impact relationships in the model are all positive. The order of impact from strong to weak on the LB variable is LP (0.506) > LF (0. 253 ). The order of impact from strong to weak on the LP variable is UB (0.342) > EF (0.253) > NF (0.229) and LF (0.192) Table 6. R – square, f – square. R-square R-squareadjusted LB 0.337 0.323 LP 0.743 0.734 f-square EF -> LP 0.098 LF -> LB 0.051 LF -> LP 0.087 LP -> LB 0.215 NF -> LP 0.092 SF -> LP 0.017 UB -> LP 0.287 LF x LP -> LB 0.092 Test the coefficient of determination R2: This coefficient describes the level of explanation of independent variables on a dependent variable in the model. It measures the model's prediction accuracy, fluctuating from 0 to 1; the closer to 1, the higher the level of explanation for the dependent variable and vice versa (Table 6). The adjusted R square of LB is 0.323, so the independent variable LP explains 32.3% of the variation of the variable LB. The adjusted R squared of LP is 0.734, so the independent variables NF, UB, EF, SF, and LF explain 73.4% of the variation of LP variable. Results also from Table 6 show: Independent variables of LB: The LP variable has a medium impact, and the LF x LP variable has a small impact. Independent variables of LP: variable UB has a moderate impact, variables EF, LF, and NF have a small impact, and variable SF has no impact. Test the relevance of forecast Q2 : The validated Prediction (Q2) method was used to measure the predictive fit of the structural model (J. F. Hair et al., 2014). It is the criterion for evaluating the cross-validated predictive significance of the PLS path model. Q index 2 is considered an index to evaluate the overall quality of the component model. The study uses Blind folding analysis of Smart PLSto obtain this coefficient. Then, evaluate based on the following levels: • 0 < Q2 < 0.25: low level of forecasting accuracy 9576 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate • 0.25 ≤ Q2 ≤ 0.5: average level of forecast accuracy • Q2 > 0.5: high level of forecast accuracy The Q2 coefficient is a standard to determine the model's predictive ability. The Q2 coefficient values of the LB and LP variables are 0, 190 and 0, 466, respectively, more significant than the value 0. It shows that the research model is good quality and appropriate, with a correlation between the dependent and independent variables. Table 7: Results of the Q2 coefficient in the model. SSO SSE Q² (=1-SSE/SSO) EF 720.000 720.000 LB 720.000 583.329 0.190 LF 1008.000 1008.000 LP 720.000 384.285 0.466 NF 720.000 720.000 SF 720.000 720.000 UB 576.000 576.000 Table 8. Results of testing research hypotheses. Hypothesis Stated Sampleav erage (M) Standard deviation (STDEV) T statistics (|O/STD EV|) P values Forecast H1 Natural factors of the land plot haveanimpact on land price 0.228 0.074 3,093 0.002 Accept H2 Urbanization factorshaveanimpact on land prices 0.332 0.069 4,942 0.000 Accept H3 Economic factorshaveanimpact on land prices 0.245 0.092 2,738 0.006 Accept H4 Social factorshaveanimpact on land prices 0.078 0.065 1,132 0.258 Does not accept H5 Legal factors affect land prices 0.207 0.097 1,987 0.047 Accept H6 Legal factors affect the relationship between land price and land bank 0.132 0.057 2,439 0.015 Accept H7 The Land Price factor has an impact on the Land Bank 0.478 0.188 2,694 0.007 Accept 3.3. Hypothesis Testing The results of testing the research model hypotheses are shown in Table 8, showing that all factors satisfy the criteria of reliability and validity. However, the structural model used to test the study's hypotheses is only statistically significant when p-value ≤ 0.05. Based on the criteria in testing the structural model, 6 hypotheses are accepted, and 01 hypothesis H4 (P values = 0.258 > 0.05) is rejected. All hypotheses H1, 2, 3, 5, 6, and 7 have a statistically significant relationship at the 5% level. However, hypothesis H4 is not statistically significant. 9577 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate The t-test p-value of the relationship LF x LP affecting LB is 0.015 < 0.05, showing that the product LF x LP impacts LB. Thus, the Law plays a role in regulating the relationship between Land Price and Land Bank. The original Sample regression coefficient (O) = 0.140 > 0 shows that increased legality will increase the impact of land prices on the land bank. 4. Discussion Table 5 shows that Land Price substantially impacts the Land Bank (Original sample: 0.506). Land price is a fundamental factor in forming a land bank. Therefore, the government of Vinh Long province needs to promote and effectively implement legal guidelines and policies on land prices, such as redefining land pricing methods, promulgating land price zones consistent with market prices, Control the real estate market through policies on investment in housing projects and real estate business. The Land Price factor has a great influence on the Land Bank. Determining high or low land prices in market economy conditions is not a simple task, needs to be done scientifically and needs to be synthesized and researched. Research from many different factors and groups of factors to be able to give accurate results about land prices. Besides, in order to reduce conflicts in the issue of determining compensation land prices when the State recovers land, how can eliminate the system of land prices that depend mainly on the State policy. Next is the Legal factor, which has an impact coefficient of 0.350 on the Land Bank. It proves that the state manages land with strict laws and regulations, from land allocation, land leases, and bidding for project implementation to investment licensing, construction, and taxation. Therefore, the government of Vinh Long province needs to promote and effectively implement land law guidelines and policies on land bank creation. The local authoritiesneed to promote and effectively implement land law guidelines and policies on creating land bank, ensuring investment attraction through capital support and conditions to have legally clean premises for investment implementation. Vinh Long goverment and speculators ought to sanction an approach to compensate land value in agreement with advertise costs, back misfortune of salary, misfortune of business and other harms for land owners whose properties are recuperated by the State. When financial specialists select high-value land as an venture area, they must compensate the State for the esteem of misplaced agrarian land assets. Such an approach to recompense, support, and resettlement will clarify the duties of each party. The level of urbanization affects land prices with an Original sample index of 0.342. In recent years, the government of Vinh Long province has made efforts to implement many investment projects in transport infrastructure, urban development, and industrial parks, and the immigration rate to Vinh Long is increasing. The land plot's geographical factors include location adjacent to roads near the urban center and other economic factors, such as bank interest rates, average income, speculation, and land buying and selling. It is also why local land prices soar; the cost of creating a land bank will increase, causing financial difficulties. That's why the state needs policies to control speculation and "virtual" land buying and selling, regulate bank interest rates, impose taxes on land areas that increase in value due to urbanization, and have funding sources to create another local land bank. 5. Conclusions The study's primary purpose is to evaluate the creation of a land bank through factors affecting land prices in Vinh Long, Vietnam. Research results have identified four factors, namely, nature, urbanization, economics, and legality, that affect land prices, which are intermediate factors that directly impact the creation of a local land bank. Analysis results show that 32.3% of the fluctuations in land banks can be explained by 5 factors (Land price, Nature, Urbanization, Economy, and Legality). Land price is a direct impact factor with an impact coefficient of 0.215. Next, the Urbanization factor affects land prices with a coefficient of 0.287. The remaining Economic, Natural, and Legal factors affecting land prices have coefficients of 0.098, 0.092, and 0.087, respectively. It shows that land price and urbanization factors are essential in creating a land bank in Vinh Long. The remaining 67.7% is explained by factors other than the model, such as planning and financial factors. The research has made several significant contributions to perfecting theories and methods of state 9578 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9568-9578, 2024 DOI: 10.55214/25768484.v8i6.4046 © 2024 by the authors; licensee Learning Gate management of land, providing solutions to improve the effectiveness of state management in this field in the Vinh Long area. Besides the results achieved, the above study has limitations, such as the convenience sampling method, so the research sample does not represent all other factors. Therefore, the authors propose using the random quantity collection method in the following research direction. Besides, the new topic only focuses on organizations and individuals performing state management tasks and scientific research on land in the Vinh Long area. The following new research direction may be to expand the survey scale of non-state corporations and companies in other locations to compare research issues across regions. Acknowledgements: I would like to show my special thanks to my supervisor, Professor Vo Quang Minh, for his invaluable expertise in formulating the research questions and methodology. I would like to acknowledge my colleagues who are working at Vinh Long Natural Resources and Environment for their wonderful collaboration. I would also like to thank my tutor, Professor Nguyen Ngoc Duy Phuong, for his valuable guidance throughout my research. In addition, I would like to thank my parents for their wise counsel and sympathetic ear. Finally, I could not have completed this paper without the support of my friends, Thuan and Luan, who provided stimulating discussions as well as happy distractions to rest my mind outside of my paper. Copyright: © 2024 by the authors. 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