The housing market is a high-risk industry with too many factors influenced by the outside world, and if these risks are not effectively managed, it will bring serious impacts on the stability of the economy and society. This paper takes the risk management of housing market as the research object, utilizes the information entropy model and ANP combined method to combine and assign the risk indicators, and constructs the cloud model of housing market risk evaluation. And the cloud model is applied to the GJ housing case for practical evaluation, and it is found that the cloud droplets of the comprehensive risk of the GJ housing market are mainly distributed in the medium-risk interval from 31 to 54, focusing on 41, and a small number of risk cloud droplets appear in the higher-risk cloud map, which is in line with the actual evaluation results, and further verifies the feasibility and validity of the method. Finally, based on the evaluation results, the risk control strategy of the relevant housing market is proposed, which can be used as a reference in the development process of subsequent housing projects.

Abstract Housing is a social and developmental issue of public concern, which is related to the personal interests of every household and the people’s “living in a home”. The article quantifies the housing choice behavior of urban residents by the ring index of residential housing purchase price, and solves the problem of sample selection error by using the propensity to match score to match the experimental group and the control group. A double difference model is used to analyze the net effect of the implementation of housing market regulation policies on urban residents’ housing choice behavior. Parallel trend test in the experimental and control groups before and after the implementation of housing market regulation policies in 2018 shows significant differences, the ATT value of HSB after propensity score matching is positive, and the sample mean of the control group is 0.452 lower than that of the experimental group. The coefficient of the effect of housing market regulation policies on the behavior of urban residents’ housing choices is 0.351 and shows significance at the 1% level. The active implementation of housing market regulation policy can help optimize the housing purchase price index, which in turn stimulates the housing choice behavior of urban residents and promotes the stable and healthy development of the housing market.

Improving the repeat purchase intention of customers of real estate enterprises from marketing strategies is increasingly important for real estate enterprises to achieve the goal of stable and increasing long-term profits. This paper, driven by digital marketing technology, takes customer repeat purchase behavior in residential commodity market as the research object. The variables of brand community value, customer fit and repeat purchase behavior are first defined and selected. Then the relationship model of brand community value, customer fit and repeat purchase behavior was established, questionnaire research was conducted on mature real estate community members, and data analysis and structural equation model fitting were carried out using SPSS and AMOS software in order to test the mediating effect of customer fit in repeat purchase behavior in the residential market as well as the path. The study concluded that the standardized coefficient of customer fit on customers’ repeat purchase intention is 0.36, which has a significant positive effect. Meanwhile, customer fit has a mediating effect between the five dimensions of brand community value and repeat purchase behavior, and all five dimensions have a partial mediating effect in the process of influencing repeat purchase intention.

Accompanied by the high speed of infrastructure construction in China’s major cities, China’s construction industry has been growing rapidly, and the engineering materials industry in construction has begun to transform from weak to strong. The study constructs a systematic theoretical model of engineering materials innovation in the construction industry to realize a systematic understanding of the engineering materials innovation mechanism. Combined with the economic growth theory, the impact of engineering materials innovation on regional economic growth in major Chinese cities is explored by using the threshold regression model. Through the study, it is found that the current innovation of China’s engineering materials industry is mainly facing two major problems, namely the slowdown of industrial growth rate, and the reduction of fixed asset investment, while the diversification path of engineering materials technological innovation outputs is beginning to diverge, and the diversity and ubiquity indices are gradually increasing, but the growth rate is slowing down. Threshold regression shows that when engineering materials innovation every 1% growth, for regional economic growth (GDP) to promote the role of between 0.099%~0.112%. The research in this paper provides a new perspective and comprehensive explanation for the study of engineering materials innovation-driven economic development, enriches the theoretical study of regional economic growth, and also provides theoretical insights and practical guidance for the government’s policy making and enterprises’ innovation practice.

Economic development and technological progress have made people’s demand for indoor environments higher and higher, and designing safe, comfortable, and healthy indoor environments through multisensory design is worthy of serious consideration. In order to realize the assessment of indoor environment comfort under multi-sensory design, this paper carries out data assessment from three dimensions of thermal environment, light environment and air environment. Relying on the PMV model of thermal environment comfort, we analyze the trends of indoor temperature and humidity, average radiant temperature, and PMV values. Combine Weber-Fechler and lighting coefficient to study the visual comfort change of indoor light environment, and analyze the lighting illuminance of indoor light environment under different working conditions by illuminance uniformity. From the basic principle of indoor wind environment, the changes of effective temperature difference and blowing sense caused by air temperature and wind speed are investigated. The temperature difference of the south-facing room decreased between 12.14% and 15.65% before and after the heating was stopped, which is a small change. The average indoor ambient radiant temperature values at different measurement points were up to 27.59 °C, and the fluctuations of the average indoor thermal ambient PMV values were within the range of [-1.2,1.6]. When the indoor light ambient illuminance value increased from 200 lux to 600 lux, the visual comfort improvement was more significant than from 600 lux to 1000 lux. The indoor air distribution characteristic ADPI value was 100% when the air velocity was 0.15m/s and 0.25m/s. Based on the results of indoor environment comfort assessment, multisensory optimization of indoor environment comfort from heat, light and air environments is proposed to provide guidance for improving people’s satisfaction with indoor environment.

Green transformation is a necessary path on the road of green development of small and medium-sized enterprises (SMEs), and it is also a top priority for implementing the concept of sustainable development. The article innovatively proposes a green transformation evaluation method for urban housing projects based on the ecological footprint theory, which clarifies the resource use of urban housing projects during the construction process through the ecological footprint calculation, clarifies their ecological deficits, and solves the reduction of their ecological footprints through the green transformation. A double-difference model is introduced to analyze the impact of SMEs’ green financing and issuance of green bonds on the green transformation of urban housing projects. The short-term market response of green bonds issued by SMEs with green financing and the environmental benefits of urban housing projects are also verified. Between 2012 and 2021, the ecological footprint of urban housing projects in Guangdong Province increased by 36,273,366 nha, an increase of 137.04%, and the reduction of ecological footprint in the planning and construction phase of urban housing projects in Guangdong Province after the implementation of the green transformation strategy totaled 10,377.85 nha. Whenever the level of SMEs’ green financing and issuance of green bonds is increased by 1%, the reduction of ecological footprint of the green transformation of urban housing projects will be increased by 1%. Transformation of the ecological footprint reduction will be enhanced by 4.059. Relying on the green financing behavior of SMEs can promote the enhancement of the short-term market effect of the enterprise, and can also effectively enhance the utilization rate of green building materials in urban housing projects, and help the green transformation of urban housing projects.

Small and medium-sized enterprises (SMEs) in Ukraine are increasingly challenged by ongoing economic instability, characterized by currency volatility, inflationary pressures, and uncertain political dynamics. This paper explores the role of management in the socio-economic development of small and medium-sized businesses (SMEs) under conditions of instability. Time series from 2001 to 2023 is analyzed for Ukraine. ADF and PP unit root test shows that data is stationery at first difference. ARDL bound test shows that time series data is cointegrated. Long-run estimates of ARDL technique show that management practices, socioeconomic development, research & development, technology adoption and employee skill & training have significantly positive relationship with SMEs growth rate, whereas, economic instability decrease the SMEs growth rate in Ukraine during studied time. Government of Ukraine should reform and bring new policies in all sectors especially manufacturing and production sector. Digitalization of the economy is foremost requirement of the Ukraine.

Reinforced polymer composites are widely used in engineering applications due to their excellent mechanical properties, and this paper combines experiments and numerical simulations to fully examine the properties of the composites. The article takes the reinforced concrete structure of civil engineering as the basis for experimental testing on the basic properties of carbon fiber composites in its application. Subsequently, the intrinsic model of reinforced concrete and carbon fiber composites was constructed, and each finite element unit of the member was selected to simulate and finite element analysis the performance of the composites through ABAQUS computer-aided design software. The results show that increasing the diameter of carbon fibers will reduce the number of carbon fibers at the same time, which will reduce the toughness of the composite material, random and chaotic distribution of carbon fibers for the improvement of the compressive properties of concrete can achieve a relatively balanced effect, and the high elastic modulus carbon fibers for the improvement of uniaxial and cyclic compressive properties of concrete play a dominant role. In addition, the oriented elliptical fibers in the long-axis direction have a significant increase in the modulus of elasticity and strength of the material, and this phenomenon will become more and more obvious with the increase of the length-to-diameter ratio. The reasonable structural design may help to improve the performance of composites and enhance their adaptability for engineering applications.

In the context of the development of regional tourism, the planning pattern of accommodation facilities in tourist attractions plays a very important role in enhancing tourists’ experience perception, which is conducive to the enhancement of tourists’ goodwill towards tourist attractions. This paper is oriented to the theory of sustainable development, and selects GL city as the research case site to obtain the visitor evaluation data of accommodation facilities in tourist attractions. The evolution trend of tourist attraction accommodation facilities is analyzed by geographic concentration index, standard deviation ellipse, nearest neighbor index, combined with TF-IDF algorithm to extract high-frequency words of tourists’ experience on the planning of tourist attraction accommodation facilities, and coarse-grained tourism review text sentiment classification model is introduced to analyze the tourists’ sentiment evaluation of tourist attraction accommodation facilities. The geographic concentration index of accommodation facilities in tourist attractions increases from 20.19 to 53.72 from 1980 to 2023, and the relative frequency difference between “service” and “room” of hotel facilities in tourist attractions is 4.35 percentage points, while that of B&B is only 0.5 percentage points. The relative frequency of “service” and “room” of hotel facilities in tourist attractions differed by 4.35 percentage points, while that of B&Bs differed by only 0.77 percentage points, and more than 70% of the tourists had a positive attitude towards the experience of accommodation facilities in tourist attractions. Lodging facilities in tourist attractions need to enrich the sequence of lodging products, improve the layout of service and reception facilities, create relevant themed B&Bs, and enhance the service quality of lodging facilities by combining modern technology to further promote the sustainable development of lodging facilities planning.

As an important variable in China’s economic system, real estate price is a key point closely linked to the country’s economy and people’s livelihood. It is of great practical significance to study the impact of socio-economic factors in the housing market on China’s house prices and its transmission mechanism, so as to put forward corresponding policy recommendations for stabilizing house prices and preventing real estate market and financial risks. Taking China’s time series data from 2014 to 2023 as a sample, this paper empirically investigates the law of the relationship between socio-economic factors in the housing market and house price fluctuations by constructing a structural vector autoregressive model, and employs impulse response function and variance decomposition to analyze the relationship between the two dynamically. The study shows that the 15-period variance decomposition effect of itself and residents’ disposable income are the main influencing factors of house price fluctuation, and the degree of explanation of its fluctuation is 55.5% and 23.2% respectively. In a market economy, real estate prices fluctuate up and down around their value due to the law of value. Therefore, real estate prices cannot rise indefinitely, and the final result should be stabilized within a reasonable level.