## 5 Results and Discussion
### Result of Analyzing the Factors Influencing Housing Abandonment  
We discovered the spatial autocorrelation for the distribution of abandoned houses. Thus, we selected SEM, because the model was found to be most suitable after testing its multicollinearity, goodness of fit, and explanatory power. The analysis results are shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Variables** & **Spatial Error Regression Model (SEM)** \\ \hline Spatial effects & \(\lambda\)(Lambda) & 0.91 *** \\ \hline \multicolumn{2}{c}{Constant} & \(-13.875\) *** \\ \hline \multirow{6}{*}{Characteristics of physical topography} & Presence of abandoned houses on the & \multirow{2}{*}{13.437 ***} \\  & boundaries of administrative divisions & \\ \cline{2-3}  & Distance from a river to the abandoned & \multirow{2}{*}{\(-0.001\) *} \\  & houses & \\ \cline{2-3}  & Presence of abandoned houses in the & \multirow{2}{*}{\(-3.917\) **} \\  & vicinity of a river & \\ \cline{2-3}  & Average slope & 6.037 *** \\ \hline \multirow{6}{*}{Characteristics of environmental location} & Number of businesses discharging & 0.088 *** \\  & pollutants & \\ \cline{2-3}  & Number of industrial complexes & 2.023 \\ \cline{2-3}  & Number of businesses in the industrial & \multirow{2}{*}{0.300 *} \\  & complex & \\ \cline{2-3}  & Amount of wastewater generated & 2.097 *** \\ \cline{2-3}  & Amount of wastewater discharged & 2.177 *** \\ \cline{2-3}  & Distance to abandoned houses in the & \multirow{2}{*}{\(-0.001\) **} \\  & vicinity of a wastewater treatment facility & \\ \cline{2-3}  & Presence of abandoned houses in the & \multirow{2}{*}{3.048 *} \\ \cline{2-3}  & vicinity of a wastewater treatment facility & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model estimation results. Figure 8: LISA cluster map. First, we examined the physical and environmental factors in depth. The effect of a location's environmental characteristics on housing abandonment was found to be significant under the following circumstances: a high number of businesses that discharge pollutants, a high number of businesses within an industrial complex, a larger amount of wastewater generated, a larger amount of wastewater discharged, closer proximity to a wastewater treatment facility, and being located in the vicinity of a wastewater treatment facility. Under these circumstances, housing abandonment was more likely to occur and cause further housing abandonment in neighboring areas. This indicated that the likelihood of housing abandonment can increase when the deterioration of surrounding environment worsens. With regard to the variables related to topography, we found that the cases of housing abandonment increased when the houses were located on the boundary of administrative divisions and in the vicinity of a river. The closer the house was to a river, the more likely it was to be abandoned. [PERSON]'s study finding showed [10] that houses on a steep slope were likely to be abandoned. To sum up, poor topographic locations increased the likelihood of housing abandonment. Next, we examined demographic factors. Similar to findings in previous studies by [PERSON] and [PERSON] [20] and [PERSON] and [PERSON] [22], we found that population of the elderly over 65, the number of recipients of the National Basic Living Security Allowance, and adjusted death rate had significant effects on housing abandonment. It appeared that houses became abandoned because the increase in the population of the elderly residents and poverty-stricken residents lowered the demand for housing and inhibited the utilization of the area. It was found that a 1% increase in the death rate due to aging or other reasons increased housing abandonment by 0.38%. Finally, we examined the effect of industrial and economic characteristics on housing abandonment, and found that housing abandonment was significantly affected by the number of businesses, fluctuation rate of land price, total out-migration, and housing supply ratio.