Buildings In Areas Research Articles

HEDONIC PRICING MODEL (HPM) ON SOUTH TANGERANG RESIDENTIAL PROPERTY VALUE

This study investigates the effects of location, structural, and environmental attributes on residential property values in South Tangerang City, Indonesia. The research employs the Hedonic Pricing Model (HPM), formulated mathematically using the multiple linear regression approach to determine the relative contribution given by these attributes. To achieve the objective, data were collected from information on residential properties in South Tangerang City which is accessible on various property buying and selling websites. The data collection was limited from July 2023 to January 2024. The results showed that some variables affected the value of residential properties, such as distances to KRL stations, public parks, top high schools, and the Central Business District (CBD), as well as building areas, land areas, and the number of rooms (bathrooms and bedrooms). However, other variables, such as distances to malls, hospitals, universities, and population density, had no partial effect on residential property values. If we look at the types of variables, the standardized coefficient beta test revealed that building areas were the most dominant variable affecting the property values in the region. This finding is different from other results, showing that property values are local. The influence of property attributes can vary across regions, so the impacts and relationships are different, too.

Analisis Perhitungan Pajak Bumi dan Bangunan Berbasis Data 3D Menggunakan UAV dan LiDAR di Kecamatan Baso, Kabupaten Agam

Land and Building Tax (PBB) calculations using conventional methods often face obstacles in terms of accuracy and efficiency. This research implements UAV (Unmanned Aerial Vehicle) technology integrated with LiDAR sensors to optimize PBB calculations in Baso District, Agam Regency in 2024. The research method used is applied research with a quantitative approach, using DJI Terra and TerraSolid software for data acquisition and processing. The results showed that UAV-LiDAR technology is able to produce 3D spatial data that presents detailed information about the dimensions of tax objects, including area, volume, and height of buildings. Compared to conventional 2D methods, this technology shows significant advantages in assessing multi-storey buildings and complex structures. The resulting point cloud data supports precise object classification and more accountable tax assessment. Although its implementation requires high initial investment, the technology proves effective in improving the validity of tax object data and strengthening the PBB calculation base. This research contributes to the modernization of local taxation system through the utilization of the latest geospatial technology.

Field experimental study of the impact of solar radiation on the thermal comfort of occupants near the glazing area in an office building

Field experimental study of the impact of solar radiation on the thermal comfort of occupants near the glazing area in an office building

ANALISIS EARNED VALUE TERHADAP BIAYA DAN WAKTU GEDUNG DIREKTORAT POLITEKNIK PU SEMARANG DENGAN PRIMAVERA P6

Semarang PU Politeknik Building Construction Project is the construction of an educational building area developed in collaboration between PT Wika Gedung and PT Yodya Karya with a construction management consultant contract value of 3.9 billion and an executing contractor contract value of 281 billion. In this research, a research method with a quantitative descriptive approach was taken. This means that in this project, the condition of the project is described based on the analysis of the data obtained from the project. The data is then processed using descriptive and analytical methods. The Earned Value or EV concept method studies comparing the value of the work completed against the budget provided to carry out the work. The result of this research is to analyze using the Primavera P6 program whether the project is running at an appropriate cost and a schedule that does not deviate from the contract. The results of the analysis obtained for the implementation of the weekly analysis of the Earned Value indicator value obtained is positive which means that in the entire week of analysis, the project runs faster and more economically. The EAC and ETC values obtained are smaller than the contract cost, indicating that the project is planned to be completed at a smaller cost. The deviation value obtained is at an average of 0.08% with the smallest deviation being 0.0794% so the project will experience a profit of 8% on the completion date.

Field Measurements of Building Air-Conditioning Heat Rejection and the Thermal Environment in Urban Areas

In recent years, the surge in air-conditioning ownership and usage has led to significant heat rejection, impacting the surrounding atmosphere. Despite this, studies examining the spatiotemporal effects of air-conditioning heat rejection at a block scale remain limited. Additionally, comparative studies on the role of building areas with air-conditioning systems versus natural underlying surfaces in the urban thermal environment are relatively scarce. This study employs field measurements and ArcGIS technology to investigate the local thermal and humidity environments, as well as the spatiotemporal distribution of heat rejection from air-conditioning systems in Wuyi Square, Changsha. Results show that cooling tower exhausts in commercial buildings maintain relative humidity levels of 95.2% to 99.8% during the day, enhancing surrounding humidity. At night, the humidity aligns with atmospheric levels (from 50.3% to 62.5%). The cooling tower exhaust temperature is approximately 2.2 °C lower during the day and 2.4 °C higher at night compared to the surrounding temperatures. In contrast, exhausts from split-type air-conditioning units in residential buildings have an average relative humidity about 14.2% lower than the atmosphere humidity, with temperature averages being 5.2 °C higher during the day and 6.5 °C higher at night, raising surrounding temperatures. The study also finds that natural surface areas are up to 3.1 °C cooler and 9.6% more humid compared to built environment surfaces. Furthermore, residential areas have air temperatures about 0.3 °C higher than commercial zones, with a humidity distribution approximately 0.5% lower. These findings offer a theoretical foundation for enhancing urban thermal environments and informing urban planning and design.

MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.

Recommendations for the operation of residential premises as a way to enhance their reliability and safety

The Rosstat data shows a steady increase in the volume of residential premises being put into operation. Notably, the number of individual housing units has grown significantly, and the total area of multifamily buildings commissioned in 2023 reached a record high in the Russian Federation. A multifamily building is considered as a complex structure composed of individual apartment units, engineering systems, stairwells, elevators, attics, basements, and other structural elements. Property owners lack knowledge about the parameters and features of their residential premises, locations of load-bearing structures, and critical engineering systems. In most cases, residents do not seek assistance from specialized organizations for developing a renovation or reconfiguration project. Transformations may lead to changes in loads on the supporting structures and other systems, thereby providing certain risk for the entire residential building. In order to prevent serious consequences, the present paper suggests the “Residential Premise Operation Guideline” that outlines key parameters and recommendations to be introduced. In the long term, it should reduce the likelihood of emergency situations caused by the actions of the owners.

Forest cover change mapping based on Deep Neuron Network, GIS, and High-resolution Imagery

With the rapid advancement of technology, monitoring forest cover changes has become increasingly quantifiable through various techniques and methods. In this study, we developed a procedure that utilizes the Deep Neuron Network (DNN) model and the Geographic Information Systems (GIS) based on high-resolution imagery captured at different time points to create forest cover change maps in Nui Luot, Chuong My, Hanoi. Two RGB (Red-Green-Blue) spectral images were captured by Unmanned Aerial Vehicle (UAV) at two different time points (pre-scene and post-scene) and used to extract information for the DNN model to produce land cover maps for these two time points. The land cover classification was divided into four classes: (1) Trees, (2) Vacant, (3) Built area and others, and (4) Water surface. Combined with GIS analysis, the forest cover change maps were developed to quantify detailed increases or losses in forest cover based on the "Trees" class. The model's accuracy was evaluated using parameters such as the area Under the ROC Curve (AUC), Accuracy (ACC), Precision, Recall, F1-Score, Kappa, and Root Mean Square Error (RMSE). The analysis results indicate that from January 31, 2023, to October 20, 2023, the forest cover in the study area decreased by 0.53%. The accuracy metrics for the pre-change scene were: average AUC = 0.922, ACC = 76.86%, average Precision = 0.743, average Recall = 0.73, average F1-Score = 0.723, Kappa = 0.692, and RMSE = 0.297. For the post-change scene, the accuracy metrics were: average AUC = 0.954, ACC = 81.89%, average Precision = 0.823, average Recall = 0.815, average F1-Score = 0.818, Kappa = 0.758, and RMSE = 0.262. A deforestation scenario was constructed to evaluate the effectiveness of the DNN models in assessing and monitoring forest dynamics.

An Improved Method for Human Activity Detection with High-Resolution Images by Fusing Pooling Enhancement and Multi-Task Learning

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments.

Cost-Benefit analysis for energy efficiency of existing hotel building

The purpose of establishing a hotel building itself is expected to get good financial benefits as much as possible. In order to increase the cost-benefit for the building, increasing the energy efficiency is a major way to save energy and thus reduce energy expenses from the existing building. This research aim is to analyze energy consumption and cost-benefit at repaired existing hotel building. The method that used in this research are existing building modeling, energy and cost simulation, provide repairmen alternatives to reduce energy consumption and cost, and determine the optimum alternative design. The simulation using Sefaira as a Sketchup Software plug-in. In this research, the object is hotel building that have 11 floor and 1 basement at Yogyakarta, Indonesia. The result show that the existing hotel building consume 977 kWh/m2/year and energy cost IDR 2.994.403.821. The alternative for repairmen existing hotel building area the replacement of tint window, insulated wall application, skylight addition, and window addition at dismantle room area. For the selected alternative is the hybrid of window addition at dismantled room area and insulated wall that decrease energy consumption and cost into 871,06 kWh/m2/year (12,16%) and IDR 2.346.637.473 respectively. It means the energy cost savings is IDR 272.042.266 (9,09%). Furthermore, from cost-benefit analysis resulted the economic internal rate of return (EIRR) 13,61%, payback period in 5,52 years. In 20 years, PV Cost IDR 14.493.532.315,84 and PV benefit IDR 16.422.494.153,10, resulted the NPV at IDR 1.928.961.837,26 and BCR value 1.13.

Quantifying spatial patterns of urban building morphology in the China’s Guangdong-Hong Kong-Marco greater bay area

ABSTRACT Understanding the spatial patterns of urban building morphology is crucial for revealing the interplay between the built and social environments. Previous research has predominantly concentrated on the computation of building-level metrics which makes it challenging to quantify and compare spatial patterns and variations across different cities. Using the newly available world settlement footprint 3D (WSF3D) data, this study examines the spatial patterns of urban building morphology across various cities within the Guangdong-Hong Kong-Macao Greater Bay Area, a rapidly urbanizing region in China. Specifically, we applied a concentric ring approach to delineate the spatial gradients of the building fraction, area, height, and volume from the urban center to the suburban fringes. Subsequently, we utilized dynamic time warping and a multi-dimensional scaling technique facilitating a comparative analysis of these gradients across cities. Developed cities demonstrated more homogenous distributions of building morphologies; however, notable differences were observed among the spatial patterns of distinct building metrics. Furthermore, a correlation between the spatial patterns of urban building morphology and degree of urban development was revealed, suggesting that developed cities exhibit significantly smaller declines in building morphology from the urban core to the rural periphery.

RELATIONS BETWEEN LAND AND BUILDING RECORDS AND ENTRIES IN THE LAND AND MORTGAGE REGISTER, IN THE CONTEXT OF REAL ESTATE TAXATION (AGRICULTURAL AND FORESTRY TAX)

Pursuant to Art. 21 of the Geodetic and Cartographic Law the basis for tax assessment is the data contained in the land and building register. These data concern e.g. area and land classification, character of buildings (premises), usable areas of buildings (premises), as well as the entity liable to pay real estate tax (agricultural, forestry). Undermining the probative value of the land and building register is possible only in particularly justified cases, such as when data from the records are in contradiction with data from other official documents or public registers. An excerpt from the register of land and buildings is evidence from an official document, therefore, as counter evidence allowing the tax authorities to determine the real estate tax (agricultural, forestry) based on data other than those resulting from this record, there should also be a document. This document may be a copy, excerpt or certificate from the land and mortgage register. However, evidence from the land and mortgage register will not be recognized by the tax authority in every tax case, because the information placed in specific sections of the ledger has different binding powers for the tax authorities. The aim of the article is to present the relationship between data contained in land and mortgage registers and land and building records in the determination of real estate tax (agricultural, forestry), identifying problems in this area and indicating possible directions of change.

Carbon Neutrality Benefit by Developing Underground Space and Overlying Green Land: Calculation Methodology and Case Study

The advancement of ecological civilization has heightened the urgency for urban emission reductions. Currently, there is a significant gap in the quantitative assessment of the carbon neutrality benefit of developing underground space and overlaying urban green land. To address this, this study presents a carbon neutrality benefit assessment model based on building lifecycle theory. The model quantifies the carbon emissions of typical public buildings utilizing aboveground/underground space and assesses the carbon sink impacts of green lands. Notably, it introduces the innovative parameter of characteristic time to evaluate how the comprehensive development of underground space and aboveground urban green land contributes to achieving carbon neutrality. Case analysis of a typical shopping mall in northern China revealed that, in comparison to aboveground buildings, underground buildings exhibit higher energy consumption for construction and lighting; however, they demonstrate superior thermal performance, providing warmth in winter and coolness in summer. Furthermore, urban green spaces of equivalent building area can sequester a portion of the carbon emissions produced by underground buildings, resulting in a lower overall carbon emission profile for the underground space system throughout its lifecycle compared to that of the aboveground space system. Consequently, the development of underground spaces plays a crucial role in advancing carbon neutral objectives. The study also emphasizes the importance of selecting plant communities with enhanced carbon sequestration capacities and developing corresponding green spaces to better meet carbon neutrality objectives. This research offers a novel perspective for evaluating the carbon neutrality benefits of underground space systems, providing valuable design guidance for integrating urban underground and aboveground spaces.

A simulation-based study on the energy efficiency of green façade retrofitting for different types of facades in rural China

As China’s urbanisation continues, the building area is expanding, of which the occupancy of rural residential buildings is also very large. However, most rural buildings have poor thermal performance. This paper analyses the energy-saving potential of green facades for rural buildings in China by simulating typical buildings with different types of facades in rural China. The simulation results show that indirect green façades can achieve good energy savings. Buildings with four types of facades: red brick, rubble, hollow brick, and concrete achieve energy savings of 18.39%, 17.85%, 14.47%, and 11.52%, respectively, after retrofitting with green facades.

Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs

One of the key elements in real estate appraisal of residential buildings is the usable area. To determine the monetary value of real estate, appraisers in Poland often rely on transaction data registered in the Real Estate Price Register (REPR). However, the REPR may contain meaningful gaps, particularly on information concerning usable areas. This may lead to difficulties in finding suitable comparative properties, resulting in mispricing of the property. To address this problem, we used linear and nonlinear models to estimate the usable area of buildings with multi-pitched roofs. Utilizing widely available data from the Topographic Objects Database (BDOT10k) based on LiDAR technology, we have shown that three parameters (building’s covered area, building’s height, and optionally the number of storeys) are sufficient for a reliable estimate of the usable area of a building. The best linear model, using design data from architectural offices, achieved a fit of 95%, while the best model based on real data of existing buildings in the city of Koszalin, Poland achieved 92% fit. The best nonlinear model achieved slightly better results than the linear model in the case of design data (better fit by approximately 0.2%). In the case of existing buildings in Koszalin, the best fit was at 93%. The proposed method may help property appraisers determine a more accurate estimation of the usable area of comparative buildings in the absence of this information in the REPR.

COMPARATIVE STUDY OF POLYSTYRENE MODIFIED WITH Sand apricot (SA) SEED OIL and Uvaria chamae (UC) SEED OIL

In order to improve the utilization of polystyrene in the area of packaging, consumer electronics, building, medical, and food service disposables, it is necessary to modify some important properties. This paper focused on the comparative study of polystyrene modified with Sand apricot (SA) seed oil and Uvaria chamae(UC) seed oil. Oil from the two seed was isolated, the quality parameter determined and the comparative analysis on some mechanical properties of polystyrene blended with Sand apricot and Uvaria chamae seed oil determined.The findings on the quality of the SA and UC seed oil are as follows: oil content (10.39% and14.56%), specific gravity at 25 oC (0.931 ± 0.21 and 0.87 ± 0.10), acid value (11.083 ± 0.18mg KOH and 9.028 ± 0.32 mg KOH), iodine value (20.798± 0.40 and 10.807 ± 0.40), peroxide value (12.30 and 7.2), free fatty acid (6.30% and 4.015%), and saponification values (283.271 and 173.392). The mechanical properties of the polystyrene blend with SA and UC seed oil (PS-SA and PS-UC) at 100g were reported as follows; ultimate tensile strength (58.6 and 50.4MPa), Young modulus (1954.0 and 1986.4MPa), % elongation (13.4 and 12.3%), break load (500.6 and 300.6 N), and Shore D hardness (80.0 and 79.0), The findings revealed that the mechanical properties of PS-SA and PS-UC were highly competitive with the conventional white petroleum oil used as a plasticizer. As a recommendation, Sand apricot and Uvaria chamae seed oil should be used as a plasticizer since it is available, cheap, and environmentally friendly.

A Case Study on Sustainable Technologies in Residential Buildings from a Life Cycle Cost Analysis (LCC) Perspective

The article mostly addresses the application of sustainable technologies in residential construction through life cycle cost analysis (LCC) using the net present value (NPV) calculation method. The authors rely on data obtained through their own research and information received from the market environment. The article outputs are in the form of conclusions based on a case study on a specific building (apartment building), elaborated in several versions with respect to the technologies used. In total, there are seven alternative versions divided into two groups, where a so-called reference technology representing a traditional (standard) technical solution is present in each group so that a relevant comparison can be made. The first group includes technologies related to heating and hot water, while the second group focuses on the application of recycled water (so-called grey water). The outputs obtained provide an interesting and fact-based view of sustainable technologies within the life cycle of a building drawing from currently available information sources. At the same time, the presented analysis has incorporated price predictions for key commodities, i.e., electricity, water, gas. The article’s specific conclusions indicate that the technologies utilizing renewable energy sources (RES) are typically less economically advantageous (in the absence of subsidy sources) compared to conventional (traditional) solutions, despite the significant savings in operating costs. The LCC indicator revealed a cost value per square meter of gross floor area (GFA) for a residential building ranging from EUR 43 to 68, contingent on the specific option under consideration. This cost value was determined over a 20-year follow-up period and a real discount rate of 4%.

Machine learning application to disaster damage repair cost modelling of residential buildings

Restoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.

The Urban Park Green Spaces Landscape Premium Functional Value Accounting System: Construction and Application

Urban park green spaces have the functions of improving the urban ecological environment and providing recreational services, and at the same time, they have a certain effect on the value of the surrounding residential property. To quantitatively assess the value of the landscape premium function of park green space, many scholars have carried out research exploration and adopted a variety of methods (such as the contingent valuation method (CVM), travel cost method (TCM) and hedonic price method (HPM)), which have developed from simple theoretical models with single factors to complex empirical models with multiple factors. Among them, the hedonic price method has become the mainstream research method, and in recent years, it has been widely adopted in combination with GIS technology. In terms of research objects, single park green space or multiple park green spaces in large cities are the main focus, while there are fewer studies on park green spaces in built-up areas of small and medium-sized cities. In terms of research content, there are more studies on the value-added coefficient of landscape premium and influence distance, and there are fewer studies on the total value of landscape premium. This article aims to calculate the total landscape premium value of all park green spaces in the built-up areas of small and medium-sized cities, proposing a complete and operable accounting system for the functional value of park green space landscape premiums by combining GIS with a hedonic pricing model and remote sensing image interpretation methods. For the first time, a method for interpreting the height of residential buildings within the benefit range of landscape premium through remote sensing images is proposed, and then the floor area ratio of residential plots is estimated, so as to estimate the total area of actual beneficial buildings. Therefore, this paper takes Chifeng City, a small and medium-sized city, as a case study, and empirically demonstrates the assessment of the landscape premium function of parks and green spaces in the built-up area of Chifeng City by using this accounting system. Research shows that this method has certain feasibility, not only calculating the total value of landscape premium but also addressing the issue in existing studies where all areas within the potential range of landscape premium function are counted as appreciated areas, leading to an overestimation of the premium. It further advances the accuracy of accounting for the value of landscape premium function of urban park green space and provides theoretical reference for the planning and construction of urban park green space.

Опыт электроразведки площадки поселения бронзового века Звягино-4 (Южное Зауралье)

The paper presents the findings of electrical survey of Zvyagino-4 unfortified settlement of the Alakul culture (1700s–1600s BC). The settlement is found in the forest-steppe part of the Southern Trans-Urals. The study uses total station survey, digital surface modelling and electromagnetic profiling (with AEMP-14 electromagnetic frequency sensing equipment) and investigates the territory of the Bronze Age site. The most significant findings were identified for the common-mode component. They suggest that the upper levels reveal contours of a site of a complex geometric shape. According to the total data, this site can be associated with the localization of the cultural layer, the ashfield and the place of construction of Bronze Age buildings. The experience of electrical survey at the Alakul culture unfortified settlement Zvyagino-4 allows a hypothetical reconstruction of the Bronze Age settlement planning. It is assumed there were 5 buildings arranged in two parallel rows. The study clarifies boundaries and area of the settlement buildings (1700 sq.m.). It is noteworthy that the problem of delimitation of the area of the buildings and the area of the developed space has not yet been raised in the light of the materials of unfortified settlements. Despite the insufficient interpretation methodology the electrical survey method we use for archaeological sites can be considered successful.