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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 2: 404 - 411

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.02.014

Resource Utilization and Carbon Trading

The Willingness of Ancient Town Residents to Transfer Their Houses and Its Influencing Factors—The Case of Luomu Ancient Town of Emeishan City, China

  • FANG Mei , *
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  • Leshan Vocational and Technical College, Leshan, Sichuan 624000, China
*FANG Mei, E-mail:

Received date: 2023-10-07

  Accepted date: 2023-12-20

  Online published: 2024-03-14

Supported by

The Southeast Asian Economic and Cultural Research Center Project(DNY2309)

The Sichuan Emergency Management Knowledge Popularization Base Project(SCYJ2023-06)

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Abstract

Transferring the houses of ancient town residents for the development of home-stays is an effective way to leverage the leading role of ancient towns in rural revitalization. Based on the questionnaire survey data of residents in Luomu Ancient Town of Emeishan City, this study used a Logistic regression model to study the residents’ willingness to transfer their houses and its influencing factors. The results showed that the age of the householder, the education level of the householder, the number of migrant workers in the family and the per capita housing area have significant effects on the housing transfer willingness of the residents. The residents of ancient town pay much attention to issues such as the transferring price of the house, whether the transferring rent can be paid to the account on time, the integrity and strength of the home-stay operators, and whether there is any governmental guarantee. According to the results of the data analysis, the following suggestions were put forward: (1) Strengthening the propaganda and guidance of young and well-educated family members, so as to have a positive impact on the householder; (2) Increasing the vocational skills training of the family labor forces in the ancient town, and encouraging more family labor force to be migrant workers; (3) Relocating the resettling the residents of the ancient town in other places; and (4) Increasing investment promotion efforts to attract enterprises with high integrity and strong strength to the overall transferring operation. This would to ensure that they can withstand higher transferring prices, and it is important that multiple measures are taken to reduce the concerns of risks in the residential housing transfer process.

Key words： rural revitalization; housing transferring; willingness; Luomu Ancient Town

Cite this article

FANG Mei . The Willingness of Ancient Town Residents to Transfer Their Houses and Its Influencing Factors—The Case of Luomu Ancient Town of Emeishan City, China[J]. Journal of Resources and Ecology, 2024 , 15(2) : 404 -411 . DOI: 10.5814/j.issn.1674-764x.2024.02.014

1 Introduction

With the steady promotion of China’s rural revitalization strategy and the booming development of tourism in the post-pandemic era, the rural home-stay industry has attracted widespread attention from scholars, business, and political circles due to its unique value (He and Zhao, 2022; Wang, 2022; Mo, 2023). As a characteristic factor of rural tourism and an advantageous industry for rural revitalization, rural home-stays play a key role in optimizing the rural industrial structure, improving the rural environmental ecology, inheriting the rural folk culture, and expanding the channels for farmers to become rich (Ye and Zhou, 2023; Zhi and Yan, 2023). Many ancient towns have large numbers well-preserved houses that can evoke people’s nostalgia over the years of historical accumulation. Moreover, due to factors such as the shrinking family population structure and population outflow, many houses are idle or underutilized, so they provide a good resource foundation for the development of homestay tourism. However, due to the dispersion of property rights, and the fact that most property rights and usage rights are directly held by the homeowners, most ancient towns suffer from a lack overall planning and efficient operation that is similar to Wuzhen.
The idle houses in ancient towns are not well utilized, and some scattered homestays not only have limited capacity but also relatively poor quality, so their driving effect on tourism in ancient towns is not obvious. If these ancient buildings can be concentrated and unified into high-quality homestays through the transfer of houses, it will greatly enhance the attractiveness and retention rate of the ancient town, and play a better driving role for tourism in the related industries. This improvement will be of great significance for promoting the rural revitalization of the ancient town and surrounding areas. Most of the research on the tourism development of ancient towns focuses on the evaluation of resources and development potential. Some scholars have also conducted relevant research on the value of using ancient town houses to develop homestays. However, motivating ancient town residents to transfer their houses for the overall planning and operation of homestays is still an urgent problem that needs to be solved.
Located at the foot of Mount Emei, Luomu Ancient Town, also known as “Qinglongchang”, has a history of over 2600 years since the Qin and Han dynasties. This town is famous for cultural and tourism development in Emeishan City and enjoys the title of “Famous Mountain Ancient Town” (Liu, 2010). As a land of the ancient Shu, Luomu Ancient Town is situated near mountains and rivers, with a lingering ancient charm, ancient buildings, preserved stone roads, handmade soy sauce shops, blacksmith shops, and Chinese Academies that can still be seen everywhere (Li et al., 2014; Zhou, 2020), Numerous cultural relics, leisurely and peaceful lifestyles, and folk customs provide abundant resources for tourism development (Huang and Wang, 2019; Kang, 2019); The superior location conditions close to Mount Emei provide sufficient tourist resources for the development of tourism in this ancient town (Gan, 2010). However, like many other ancient towns, due to the lack of overall planning and construction, and the lack of good accommodation conditions, many tourists can only choose a fleeting “Taking a tour here” option, resulting in a short stay of tourists, low added value, and missed business opportunities. Centralizing and unifying these ancient buildings into high-quality residential accommodation through the transfer of houses would greatly enhance the attraction and guest retention rate of Luomu Ancient Town, play a driving role of ancient town tourism in related industries, and have important significance in promoting the revitalization of Luomu Town and nearby areas. However, stimulating the residents of ancient towns to transfer their houses for the overall planning and management of homestays is an urgent issue to be solved. In view of this issue, the author took the residents of Luomu Ancient Town as the research object to explore the theoretical basis for promoting the transfer of houses in the ancient town. This study can provide a theoretical basis and case support for promoting the development of residential tourism in the ancient town through the utilization of residential houses, help the residents to effectively utilize their housing resources to increase their income, and help the implementation of the rural revitalization strategy.

2 Research methods and data sources

2.1 Study area

Luomu Ancient Town is 10 km from the urban area of Emeishan City and only 3.5 km from Baoguo Temple of Mount Emei. It was included in the list of historical and cultural towns in Sichuan Province. The entire town covers an area of 49.9 km2, governing nine administrative villages and one community, with a resident population of 30000. The ancient town is built along the north bank of the Linjiang River, and the existing town covers an area of 2 km2. The core scenic spot is Qinglong Community, with eight streets and seven lanes. At present, the community has 10 resident groups, 661 registered residences and a registered population of 2805. At present, only five houses have been commercialized through circulation, among which three homestays of a certain quality are far from meeting the needs of tourists. During the peak tourist season, the unit price of a room exceeds 1000 yuan and there is still a situation where “one room is difficult to find”. On the other hand, many houses have been idle due to the lack of circulation, which has not fully utilized their economic value and has restricted the development of tourism in this ancient town to some extent.

2.2 Data sources

Census data and street building registration data were collected to form the overall sample of the survey and to conduct the sample survey. To ensure the authenticity and effectiveness of the survey results, a random proportional sampling method was adopted based on the total number of street house numbers. A total of 135 households were sampled, and 128 questionnaires were collected (at seven households, no one was at home during the survey). Among them, 117 valid questionnaires were collected, for an effective rate of 91.41%.
To facilitate subsequent statistical analyses, the survey variables were standardized quantitatively when the questionnaire was designed. The selection and assignment of the questionnaire variables are shown in Table 1.
Table 1 Variable selection and assignment
Variable Code Assignment specifications
Age of the householder (yr) X1 1, 2, 3, 4, 5 represent ≤30, 31-40, 41-50, 51-60, >60, respectively
Education level of the householder X2 1, 2, 3, 4, 5 represent below primary school, primary school, middle school, high school, university and above, respectively
Family size (person) X3 1-7 represent the number of corresponding persons
(where 1 represents a family that has 1 person, 2 represents a family that has 2 people, and so on)
Household labor force (person) X4 0-5 represent the number of corresponding persons
(where 0 represents a family that has no labor force, 1 represents a family that has a labor force of 1, and so on)
Proportion of household labor force X5 Total household labor force/household population (0-1)
Number of migrant workers (person) X6 0-4 represent the number of corresponding persons
(where 0 indicates that no one in the family is working outside, 1 indicates that one person in the family is working outside, and so on)
Per capita housing area (m2) X7 1, 2, 3, 4, 5, 6, 7 represent ≤30, 31-40, 41-50, 51-60, 61-70, 71-80, and >80, respectively
Per capita disposable income
(yuan person-1 yr-1)		 X8 1, 2, 3, 4, 5 represent ≤30000, 30001-40000, 40001-50000, 50001-60000, and >60000, respectively

Note: The size of the labor force refers to the number of people aged 16 to 60 who are able to work.

2.3 Survey sample statistics

In this survey, we ultimately obtained valid questionnaires from 117 households. The basic information of the sample households is shown in Table 2.
Table 2 Statistical characteristics of householders (n=117)
Variable Frequency
(person)		 Percentage
(%)
Age (yr)
≤30 4 3.4
31-40 12 10.3
41-50 52 44.4
51-60 45 38.5
>60 4 3.4
Educational level
Below primary school 0 0.0
Primary school 7 6.0
Junior high school 26 22.2
Senior high school 77 65.8
College or higher degree 7 6.0
Family size (person)
2 6 5.1
3 43 36.8
4 41 35.0
5 15 12.8
6 11 9.4
7 1 0.9
Household labor force (person)
1 2 1.7
2 53 45.3
3 60 51.3
4 2 1.7
Per capita housing area (m2)
≤30 10 8.5
31-40 15 12.7
41-50 32 27.1
51-60 28 23.7
61-70 29 24.6
71-80 1 0.8
>80 2 1.7
Annual per capita disposable income (yuan)
≤30000 4 3.4
30001-40000 19 16.2
40001-50000 58 49.6
50001-60000 28 23.9
>60000 8 6.8

2.3.1 Age structure of householders

Regarding the age of the householders, the main group includes 97 households, accounting for 82.9%, with ages from 40 to 60 years old. There are 16 householders under the age of 40, accounting for 13.7%, and only four householders aged 60 and above, accounting for 3.4%.

2.3.2 Education level of the householders

Regarding the education level of the householders, the overall educational level is not high, with 77 having high school education as the main group, accounting for 65.8%. There are 26 with junior high school education, accounting for 22.2%, as well as seven with primary school education and seven with college or higher degree education, accounting for nearly 6.0% each.

2.3.3 Family demographic structure

Regarding the perspective of family population structure, 84 households with 3-4 people are the main group, accounting for 71.8%. There are six households with two people, accounting for 5.1%. The household with the largest population has seven people.
In terms of the size of labor force, most families have 2-3 working members, including 53 families with two and 60 families with three, and the proportion of families with a labor force of 2-3 reached 96.6%. There are no non-labor households in this survey sample. The situation of migrant workers is relatively common. There is at least one migrant worker in 100 of the families surveyed, accounting for 85.5%. Only 17 households have no migrant workers, accounting for only 14.5%.

2.3.4 Family housing situation

Overall, since it is a small town, the per capita housing area of residents in Luomu Town is relatively large. There are 89 households with a per capita housing area of 41-70 m2, accounting for 75.4%. Only 10 households, accounting for 8.5%, have a per capita housing area of less than 30 m2. Only three households, accounting for 2.6%, have per capita housing of more than 70 m2.

2.3.5 Family economic situation

Since the overall economic level of Emeishan City is higher than that of Leshan City, while Luomu Town is relatively close to the county unban area with abundant cultural and tourism resources and early development, in addition to the common situation of family migrant workers, the income level of residents in the ancient town is relatively high. According to the sample verification, due to the conservative tendency of respondents in the survey, the data obtained is about 20% lower than the official statistical data. However, more than 80% of households have an annual per capita disposable income of over 40000 yuan. The main body is con- centrated from 40000 to 50000, accounting for about 50%.

3 Data analysis

3.1 Model construction

The willingness of ancient town residents to transfer their houses is defined as follows:
Y = A ( X ) + ε ,     X = X 1 , X 2 , ⋯ , X i
where, Y is the willingness of ancient town residents to transfer their houses, A is a function, Xi is the i-th influencing factor, and ε is the adjustment constants.
According to the selection of variables and assumptions, the willingness of residents in ancient towns to transfer houses Y is categorical data, and there are only two options: willingness and unwillingness, so they are the binary observation results. At the same time, a series of related factors affect the willingness of residents in ancient towns, therefore, the Logistic model is suitable for the probabilistic nonlinear regression analysis.
Let P represent the probability that ancient town residents are willing to transfer their houses, and (1-P) represent the probability that ancient town residents are not willing to transfer their houses. Taking the natural logarithm of P 1 − P, P is obtained through the regression analysis:
ln P 1 − P = β 0 + ∑ ​ β i X i
P = exp β 0 + ∑ ​ β i X i 1 + exp β 0 + ∑ ​ β i X i
where, β0 is a constant, βi is the correlation coefficient of the Xi factor, and Xi is the i-th influencing factor.

3.2 Model test

According to the constructed Logistic model, SPSS26.0 statistical software was used for data calculation and processing, and the results are shown in Table 3. The results show that among the nine indicators, the age of the householders, the education level of the householders, the number of migrant workers in the family, and the per capita housing area of the family have significant impacts on the willingness of ancient town residents to transfer their houses. The other four indicators, i.e., total household population, household labor force, proportion of household labor force, and per capita annual disposable income, have no significant impact on the willingness of ancient town residents to transfer their houses. The constructed model has R2=0.434 and P=0.001, indicating a high level of significance. Therefore, using this model to explain the willingness of ancient town residents to transfer their houses has high credibility.
Table 3 Results of the regression analysis of residents’ willingness to transfer their houses in Luomu Ancient Town
Variable name Regression coefficient Standard error Wald Degree of freedom P-value OR
Age of householder* -0.172 0.410 0.177 1 0.074 0.842
Education level of the householder** 3.846 0.848 1.231 1 0.030 46.438
Total family population 0.934 0.985 0.899 1 0.343 2.545
Family labor force -0.680 1.428 0.227 1 0.634 0.507
Labor force share 4.080 1.368 0.578 1 0.047 3.164
Number of migrant workers** 1.837 0.354 5.597 1 0.018 0.433
Per capita housing area** 1.203 0.228 0.794 1 0.043 0.816
Per capita annual disposable income -0.405 3.327 1.534 1 0.215 5.667

Note: * and ** mean the significance level is at 10% and 5%, respectively, and the same notation is used below. The term “OR” refers to the odds ratio of the regression coefficient B.

4 Results and discussion

4.1 The impact of householder demographics

4.1.1 Householder age

There is a negative correlation between the age of householders in ancient towns and their willingness to transfer their houses. The difference is significant at the level of 10%, indicating that for the residents of ancient towns, the older householders have a stronger dependence on their real houses. They are more conservative and subjectively unwilling to transfer their houses as an asset, reflecting that with the progression of age, ordinary residents become more cautious about the risk of the property attributes of their houses.

4.1.2 Householder education level

There is a positive correlation between the education level of ancient town householders and their willingness to transfer their houses. The difference is significant at the 5% level, indicating that the more educated people are, the more open-minded they are; and the more highly educated residents of ancient town are more willing to endure the market risks. At the same time, this may also indicate that the higher the education level, the lower the dependence on houses as physical assets. When the residents feel that the transfer of houses can bring the expected psychological benefits, they are willing to transfer their houses.

4.1.3 Number of migrant workers

There is a positive correlation between the number of migrant workers and the intention to transfer houses in the ancient town, and the difference is significant at the 5% level. There are two explanations for this observation. On the one hand, the large number of migrant workers brings a relatively high vacancy rate among the houses, so it is better to transfer the houses to increase income and keep the loss of occupied houses from becoming more serious. On the other hand, with a large number of migrant workers, the objective economic income will be relatively high, and the sense of economic benefits will be higher, so they are more willing to transfer their houses to bring more benefits.

4.1.4 Per capita housing area

The per capita housing area of households in the ancient town is positively correlated with the willingness for housing transfer, and the difference is significant at 5% level. This is understandable by common sense. After all, for the majority of families with a lager per capita housing area, their housing often can only meet their own use needs, that is, the housing for families may itself be a rigid demand. As for the housing transfer, it is only possible when the income from the increase in the transfer is spillover after deducting the additional cost of resolving the housing problem.

4.1.5 Per capita disposable income of households

This survey found that the annual per capita disposable income level of households in Luomu Ancient Town is generally high, but there is a negative correlation with the willingness to transfer houses and the difference is not significant. The main reason may be that the overall state of housing in the ancient town is outdated, so the current market price for housing transfer is generally low. As mentioned above, for families with a high disposable income per person, the benefits brought by housing transfer are basically negligible in the total household income, so it is better to keep the house empty and the survey showed a low willingness to transfer (negative correlation). At the same time, due to the correlations between the per capita annual disposable income of households and factors such as the proportion of household labor force and the number of migrant workers, there may be a certain degree of correlation, so the significance obtained in the statistical analysis is not very high (0.215). In addition, the data shows a high standard error and odds ratio, which is likely due to the respondents being sensitive to sharing their income data and not necessarily truthful when reporting it, resulting in survey data that is not completely accurate. In future research, other methods should be considered to obtain more practical and accurate data.

4.2 The impacts of other factors

During the preliminary trial investigation, some residents of ancient towns were found to be more concerned about the housing transfer price, the improvement of the housing structure and quality after the transfer, and the reputation and strength of the enterprises responsible for the transformation and operation of the transferred houses. They were particularly concerned that the houses may not be well utilized after being sold out, resulting in the residents not being able to obtain the expected profits, companies defaulting on contracts, and even worrying about the destruction of the housing structure leading to “Houses were damaged and possessions reduced”. In response to these issues, a multiple-choice question was added. It was initially set as a multi factor ranking question, but considering that some questions were not well ranked for decision-making, it was modified to a multiple choice question. This new question asked respondents to “please choose what issue you are most concerned about in the housing transfer (limited to three items)”. The questionnaire settings were as follows:
Please select which issue you are most concerned about in the housing transfer (limited to three) [Multiple choice question] *
□ Transfer price □ Is there a governmental guarantee?
□ Housing quality assurance □ Integrity and strength of the operator
□ Can the rent arrive on time □ How to deal with midway default
□ The length of the rental period □ Other

Note: Required questions marked with *.

The statistical results of this survey question are shown in Table 4.
Table 4 The problems of greatest concern among ancient town residents for the housing transfer
The biggest concern Frequency (person-time)
Transfer price 93
Can the rent arrive on time 71
Integrity and strength of the operator 47
Is there a governmental guarantee? 42
Housing quality assurance 35
The length of the rental period 28
How to deal with midway default 24
Other 11

4.2.1 Transfer price

Table 4 shows that the residents of ancient towns are most concerned about the transfer price of their houses. Due to the overall obsolescence of the house, the overall price of the house transfer is relatively low. At present, the annual rent per square meter is only about 30 yuan, so for a 100 m2 house, the annual rent for individuals is only about 3000 yuan. Compared to the income level of local residents, this amount is not very attractive, which is also one of the main reasons for the weak willingness of residents in ancient towns to transfer their houses.
In the survey, a significant difference was found in the expected unit prices of residents in ancient towns for housing transfer (Table 5), but the main component is concentrated from 26 to 35 yuan m-2 yr-1, accounting for 84%. The proportion of 26-30 yuan m-2 yr-1 is the highest, accounting for 62% (Fig. 1).
Table 5 Statistics of the expected price of housing transfer
Variable Mean
value		 Number of cases
(person-time)		 Standard deviation Median Mean standard error Minimum value Maximum value Skewness Deviation standard error
Value 2.33 117 0.820 2.00 0.076 1 5 1.315 0.224

Note: For the convenience of investigation, 1, 2, 3, 4, and 5 represent ≤25, 26-30, 31-35, 36-40 and >40 yuan m-2 yr-1, respectively.

Fig. 1 The expected price of residents in the old town for the housing transfer (Unit: yuan m-2 yr-1)
Further analysis of the correlations between the expected unit price of ancient town residents for the housing transfer (Table 6) and various factors revealed that the per capita housing area and annual per capita disposable income of households have a significant impact on the expected price of residential housing transfer. The per capita housing area shows a negative correlation with the expected price, but the coefficient is not very high. The main reason may be that the housing turnover of small town residents is mainly based on “units” and “rooms”, so when the total price is similar, the unit price is slightly lower for large room areas. The annual per capita disposable income of households is positively correlated with the expected price of residential housing transfer, with a relatively larger coefficient. The main reason may be that for households with a high annual disposable income, the “threshold” or “starting point” for total income is relatively high, leading to a synchronous increase in the annual expected value of housing transfer per square meter.
Table 6 Correlation analysis of expected unit price of ancient town residents for the housing transfer
Model Regression coefficient Standard error Beta t P-value
Constant 0.921 0.691 1.334 0.185
Age of householder -0.024 0.090 -0.025 -0.271 0.787
Education level 0.054 0.113 0.044 0.481 0.631
Per capita housing area** -0.133 0.050 -0.216 -2.643 0.009
Annual per capita disposable household income** 0.559 0.076 0.609 7.386 <0.001

4.2.2 Possible risks after house transfer

Whether the income from housing transfer can be realized on schedule is an issue of particular concern for the residents of ancient towns (with a response frequency of 71 times), reflecting their strong risk awareness in the housing transfer. This issue is closely related to the “integrity and strength of operators” and “whether there is governmental guarantee”, so the latter two items have also become the focus of attention for residents of ancient towns in considering housing transfer (with frequencies of 47 and 42, respectively). If these three factors are combined into a single factor of “risk guarantee for housing transfer income”, we estimate that it would be on par with the price factor.
Due to the generally old age of the houses and the fact that the structures of the houses are mainly wooden, the quality assurance of the houses after transfer is also a concern for the residents of the ancient town (with a response frequency of 35 times).
The length of the housing transfer cycle and how to deal with defaults during the process have also received attention from some residents of the ancient town (with frequencies of 28 and 24, respectively).

5 Conclusions and suggestions

5.1 Conclusions

The householders have a significant impact on the willingness of residents in ancient towns to transfer their houses. The older the householders, the weaker their willingness to transfer their houses. The higher the education level of the householders, the stronger their willingness to transfer their houses.
The family situation also has a significant impact on the willingness of residents in ancient towns to transfer their houses. The greater the number of migrant workers, the stronger the willingness to transfer their houses. The more per capita housing area, the stronger the willingness to transfer their houses. However, the per capita annual disposable income of households has little impact on the willingness to transfer houses, so there is a certain degree of contradiction.
The price of housing transfer is the most important factor that ancient town residents pay attention to in considering housing transfer. At the current price level, the expected price that the vast majority of residents would expect is from 26 to 35 yuan m-2. Whether the income from housing transfer can be realized on schedule and the quality assurance after housing transfer are also among the key concerns of residents in ancient towns, which are strongly related to the “integrity and strength of operators” and “whether there is governmental guarantee”.

5.2 Suggestions

According to the survey and analysis results, in order to enhance the willingness of residents in the ancient town to transfer their houses, promote the development of a home- stay industry with certain characteristics and quality, drive the comprehensive improvement of the tourism related industry in the ancient town, and assist in rural revitalization, measures should be considered from the following four aspects.
Firstly, increase the publicity and guidance efforts for young and highly educated family members. The age and education level of the householder have significant differences in their impacts on housing transfer, but these two characteristics themselves are difficult to change in the short term. However, it is possible to increase publicity and guidance for family members who are relatively young, have relatively high education levels, and have a certain influence on the householder, in order to enhance their willingness for family housing transfer.
Secondly, increase vocational skills training for the workforce. According to the statistics, there is a positive correlation between the number of family members working outside the ancient town and their willingness to transfer their houses, so vocational skills training for the ancient town resident labor force can lead more family members to work outside, thereby further enhancing the willingness of the ancient town residents to transfer their houses.
The third suggestion is to relocate the residents of ancient towns. Most of the existing houses in the ancient town are old and the living conditions are relatively poor. If high-quality and cost-effective houses can be rebuilt nearby, and the relocation of residents in the ancient town can be guided, more residents can reduce their physical dependence on the existing old houses, and they can get a better home and settlement in the new place, which is conducive to the overall planning of the ancient town and will boost the rural revitalization efforts.
The fourth suggestion is to increase the transfer price and reduce income risks. We can learn from the four-way linkage model of “governmental enterprise association operator” in Wenchuan County (Zhang and Ze, 2023). Local governments should increase their investment promotion efforts to attract enterprises with good financial strength, strong business capabilities, and high corporate integrity to take responsibility for the overall transfer and operation of the ancient town houses. At the same time, we can consider ways to ensure that residents of ancient town can have long-term housing transfer, such as government credit endorsement. In this way, stable and satisfactory asset returns are obtained, and there are no worries about housing safety or corporate default, thereby increasing the residents' willingness to transfer their houses.

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