1. Introduction
Housing price studies have received growing attention in recent years because of the availability of housing data and the important role of housing in human life [
1,
2,
3]. Housing prices are determined by several groups of characteristics, such as structural, locational and neighborhood attributes [
4]. Streets near houses can provide residents with opportunities for strolling and socializing [
5]. The streetscape is the “outdoor room” one encounters when turning the corner or stepping out the door into the street [
6]. The quality of a streetscape is associated with user well-being [
7]. People tend to live in houses surrounded by high-quality streetscapes with many trees or that are perceived as beautiful and safe [
8,
9]. Understanding the relationship between streetscapes and housing prices can provide new insights into the composition of housing prices and is beneficial for applications, such as urban planning and real estate.
Conventional approaches to streetscape evaluation include field surveys and interviews. These methods may lead to potential biases and are costly and time consuming [
10]. Street view imagery (SVI) has recently become widely used as an emerging source of big data [
11,
12]. The SVI is publicly available and provides an alternative to traditional methods [
11]. Owing to machine learning and deep learning, the SVI has become highly effective for assessing streetscapes in urban environments. There are two types of indicators for measuring streetscape qualities from the SVI [
13]. The first is an objective measure that describes the physical appearance of streets, such as the green view index (GVI) [
14], pedestrian volume [
15], and sidewalk length [
16]. Prior studies have mainly used objective measures to study the relationship between streetscape quality and housing prices [
8,
17]. For example, visible street greenery has been found to have a significant positive effect on housing prices [
8]. However, we can only learn about one side of a street from objective measurements. Specifically, they do not capture a street’s human perceptions, which may have subtle or complex relationships with physical elements [
18]. The second category includes subjective measures depicting human perceptions, such as beautiful, safe and enclosure [
19]. They described the raters’ overall perceptions of street view images. Analyzing the effects of the street environment’s human perceptions on housing prices may provide a comprehensive understanding of housing prices [
13]. For example, the enclosure measurement was found to have a negative relationship with housing prices [
13]. Although objective and subjective measures have distinct effects on housing prices, the strength of the association between these measures and housing prices is not fully understood.
Previous research has focused on the perceptions favored by urban planners [
13,
20,
21]. These perceptions included enclosure, human scale, complexity, imageability, safety, greenness, and walkability. These are the professional opinions of urban planners, which are difficult for ordinary house buyers to comprehend. Personal perceptions, which are more subjective than professional perceptions [
18], such as safety, wealthy, lively, beautiful, boring, depressing [
9], class, and uniqueness [
22], are closely related to residents’ daily lives. Therefore, for housing buyers and renters, these personal perceptions offer additional references and values. However, prior studies usually employed the Place Pulse dataset (Place Pulse 1.0 and 2.0) [
9] created by MIT Senseable City Lab researchers to measure the visual perceptions of Google Street View images, which may not be appropriate for Chinese cities. The Place Pulse 2.0 dataset contains 110,988 SVIs spanning 56 cities in 28 countries [
23] and does not include SVIs from mainland China. Although the Place Pulse 2.0 dataset can be applied to other cities, each city has its own socio-economic status or physical environments, such as building styles [
24,
25,
26], which may pose problems and lead to perceptual biases in people. To estimate premiums on housing prices, it would be ideal to use a perception dataset with locally obtained SVIs and local evaluations.
This study aimed to understand the impact of subjective emotional and objective view measures on housing prices using locally obtained SVIs in China through a comprehensive assessment. In this study, we extracted subjective perception and objective view indices from SVIs and systematically compared their effects on housing prices. We considered Suzhou City, China, as an example. The subjective perceptions were safety, wealthy, lively, beautiful, boring, and depressing, and they were more relevant to people’s daily lives. Locally collected SVIs were used to train a deep learning model to predict human perceptions of the street environment. The effects of subjective and objective measures on housing prices were assessed with ordinary least squares (OLS) regression. To account for spatial heterogeneity, GWR was also performed.
This study makes three main contributions to the literature. First, we comprehensively assessed the relationship between objective/subjective measures and housing prices. Second, we discussed how certain perceptions may affect housing prices. Third, we explored the effects of these measures on housing prices, both globally and locally. We do not intend to make any causal statements. Our study aims to use this correlation to demonstrate the need to incorporate human perceptions into housing price studies. Our study provides a significant reference for urban planners to enhance urban environments from the viewpoint of human perception.
5. Discussion
5.1. Effects of Subjective and Objective Measures on Housing Prices
The objective measures may generally explain more variance than the subjective measures, according to the adjusted R
2 of the OLS in
Table 3. The adjusted R
2 of Model 2 (39.26%) for OLS was 4.8 percentage points higher than that of Model 1 (34.46%). Overall, the objective measures had stronger explanatory power, and built environment factors had a greater impact on housing prices. This is in line with a study by Qiu [
13]. In addition to the sky, tree, and building view indices having the largest proportions, the plant, car, fence, and ceiling view indices also have a considerable impact on housing prices.
The global OLS model determines a single corresponding parameter for each variable in each observation. By contrast, GWR is a local model that estimates the model parameters for every observation within a local neighborhood. In
Table 3, the adjusted R
2 of Model 3 (72.3%) was greater than that of Model 4 (70.1%), which is an interesting contrast to the OLS relationship. This contradicts Qiu [
13] and demonstrates that for GWR, perceptual measures can explain more variance in housing prices than view indices on the local scale. This indicates that home buyers care more about their subjective perceptions of their neighborhoods’ surroundings.
The GWR produces a different result than OLS, suggesting that the result of the local model is opposite to that of the global model. This phenomenon is, in fact, the Simpson’s paradox. It is essential to mitigate this problem by modeling spatially varying relationships [
29]. The results of the Monte Carlo test also demonstrate that the impacts of the explanatory variables vary spatially.
Specifically, the coefficients of the boring and safety scores were both negative for OLS. For GWR, some communities had positive coefficients for boring and safety scores, whereas others had negative coefficients. Boring is a negative perception that usually has a negative impact on housing prices, indicating that a boring community environment is associated with low housing prices. Safety is a positive perception and usually has a positive impact on housing prices. This indicates that people are prepared to pay premiums for safe communities.
However, some communities had positive coefficients for boring perceptions. This appears counterintuitive. One reason is that older residential communities usually have stores on the first floor and are cheaper, whereas newer residential communities do not have stores on the first floor and are usually more expensive than older communities. Owing to stores and shoppers, older and cheaper residential communities have lower boring scores. By contrast, newer residential communities had higher boring scores. Therefore, the boring coefficients were positive for some communities.
A potential explanation for the negative safety coefficients was provided. According to
Figure 8b, the car and sidewalk view indices (53 and 48) had the highest feature importance in predicting the safety perception. More cars can bring not only safety but also more air pollution and noise. This has a negative effect on housing prices. This justification is comparable to the effect of metro stations on housing prices [
51]. The closer a community is to a metro station, the more convenient it is to travel. Thus, property prices property will increase. However, if they are too close, housing prices may be lowered due to noise and other considerations.
In addition, the effect of the tree view index on housing prices was negative for the OLS. This is counterintuitive and inconsistent with Ye’s work [
8].
Figure 11c shows that the tree view index had both positive and negative effects. The positive effects were mainly concentrated in the city center. The negative effects were in the suburbs, and a possible reason was that trees around new residential communities in the suburbs were not tall or large because of their relatively young age, resulting in a low tree view index. Thus, new communities have high housing prices and a low tree view index.
It is critical to pay attention to both the model and the selected variables when analyzing housing prices because the result of OLS is the opposite of that of GWR. In particular, the Simpson’s paradox may result from utilizing global models, such as OLS. Thus, OLS should be used with caution when attempting to explain the influence of various variables on housing prices. It is indispensable to use a local model, such as GWR, to analyze the spatially varying effects of variables.
The outcomes we obtained differed from those of Kang’s study [
9]. Kang first employed the principal component analysis technique and then used OLS to analyze the effects of various factors on housing prices. The principal components were linear combinations of individual perceptions. Kang analyzed only the principal components’ impacts on housing prices rather than individual perceptions. In addition, although Kang applied GWR, Kang did not demonstrate or analyze the spatially varying coefficients.
5.2. Pros and Cons of Subjective and Objective Measures
The GWR discloses that subjective perceptual scores can explain more variance in housing prices than objective view indices. This highlights the importance of “sense of place” and humanistic insights in evaluating the effects of various features on housing prices [
9]. Perceptual scores measure how people perceive a place psychologically and are related to the socioeconomic environment, such as land use [
52], poverty status [
53], and crime rates [
54]. Human perceptions of places depict a complete picture.
Table 3 shows that the 12 most important view indices could only explain a small proportion of the perceptual variance (17–54%). The boring and safety perceptions had the two lowest R
2 values (17% and 24%, respectively), and the two perceptions were selected using the stepwise regression model to fit housing prices. This reflects the fact that the two perceptions contain more sensory information than view indices. The multicollinearity issue with subjective measurements makes it challenging to incorporate all of them into OLS, which is one of their drawbacks.
Objective view indices can supplement subjective perceptual scores even if they cannot fully characterize a place, such as subjective measures. The view indices are distinct, unambiguous, easy to measure, and have a low correlation.
5.3. Implications for Urban Planning
This research integrates and compares the effects of subjective perceptions and objective view indices on housing prices and has broad and practical applications in urban planning. First, housing prices were significantly affected by subjective perceptions. Governments and policymakers should pay attention not only to the built environment, but also to perceptions of the micro-scale street environment around residential districts. Sidewalks and fences, which may affect residents’ perceptions of safety, should receive more attention. Currently, only the green ratio and road construction have received attention in other cities. Perception indicators should be carefully selected because of the multicollinearity problem. Second, a street environmental fee can be imposed to compensate for public funds invested in improving streetscapes [
8]. This is because real estate developers profit financially from the surrounding street environment, whereas cities create, invest in, and maintain streetscapes. Tax amounts can be determined using both subjective and objective indices. Third, boring and safety perceptions were selected to represent negative and positive perceptions, respectively. This study can serve as a reference for future research. These subjective perceptions can be used to model economics and other urban plans outside of settlement assessments. For example, they can provide new measures for street design guidelines [
38]. Urban designers and practitioners can examine the social, psychological, and emotional meanings of the street environment and better guide various applications, such as lively and safe neighborhood design, sustainable city planning, and urban micro-renovation.
5.4. Limitations and Potential Improvements
This study has several limitations that should be addressed in future studies. First, human perception may have been biased. The deep learning model used to appraise the SVIs of Suzhou was trained using SVIs collected in Wuhan City. Although both cities are located in China, each city has a unique street environment [
55]. These two datasets have distinct data distributions. To assess the performance of the model in future studies, Suzhou’s sample SVIs may need to be collected and annotated by humans.
Second, human perceptions were predicted with a deep learning model. Experiencing a place does not involve the observation of specific objects. In addition to the visual elements in SVIs, other elements are related to human perceptions. Quercia pointed out that the history, culture, interactions, and experiences of a place cannot be easily captured by images [
56]. Future research could expand the incorporation of additional datasets, such as social media datasets.
Third, previous studies have shown that the spatial scale impacts the hedonic price model’s results. The scale of the research object has a modifiable areal unit problem [
57]. A fine-scale community was selected as the study object. More coarse-level regions (such as zip code regions) can be applied in other research to conduct experiments [
2].
6. Conclusions
Using SVIs, this study analyzed the effects of subjective perceptions and objective view indices on housing prices. Subjective perceptions and objective view indices were extracted from the SVIs using deep-learning models. The effects of the subjective and objective measures were analyzed and compared using OLS and GWR. The global model OLS explored the overall impact of these measures on housing prices. GWR was employed to reveal the spatial variation in these factors from a local perspective, as opposed to the global approach that OLS uses. The main findings are summarized as follows:
First, for OLS, the overall objective measures explained more variance than the subjective measures. This result is consistent with that reported by Qiu et al. [
13]. This suggests that the built environment factors have a greater impact on housing prices.
Second, compared to the view indices, the perceptual scores for the GWR exhibited stronger explanatory power. This indicates that home buyers care more about their subjective perceptions of their neighborhoods’ surroundings. The results of the local GWR model were opposite to those of the global OLS model. The GWR result highlighted the importance of “sense of place” and humanistic insights in evaluating the effects of various indicators on housing prices. Furthermore, human perceptions of a place can provide a more complete picture.
Third, the results of the Monte Carlo test showed that the effects of the explanatory variables varied spatially and were statistically significant. However, this demonstrates the value of using GWR.
These results have important implications for governments and urban planners. They should focus on the perceptions of the microscale street environment around residential areas, as well as the built environment. Urban designers and practitioners can examine the social, psychological, and emotional meanings of street environments and guide various applications.