C/C++ device drivers to program/operate HPMIS.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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He earned a Ph.D. degree from SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), South Korea (2016). Then, he has spent a few years as a Postdoctoral Researcher and a Research Fellow at the School of Advanced Materials Science and Engineering, SKKU, and at the Center for Multidimensional Carbon Materials (CMCM), Institute for Basic Science (IBS), UNIST, South Korea, respectively. He has published >30 peer-reviewed articles/patents/books in prestigious journals/publishers such as Chem. Rev., Chem. Soc. Rev., Adv. Mater., 2D Materials, Carbon, Nanoscale, RSC Advances, ACS Omega, Royal Soc. Open Sci., JJAP, InechOpen, etc. He has published 5 books/book chapters and is serving as an Editorial Member of some scientific journals and as a member at ACS, AAAS, APS, ERMS, etc. In addition, he is a recipient of the National Postdoctoral Award for Excellent Scientists by China Government and the NSF award for outstanding young scholars (China). 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The medical imaging system plays an important role in disease diagnostic time and cost. Therefore, to manage the industry demands, the medical health-care industry is looking for high-performance imaging system that can predict and identify the disease in an early stage without the support of an expert. As the performance of these devices grows, application-specific and high-performance hardware are required to run complicated/complex medical applications.
\nPrecisely managing the medical information from multiple imaging equipment, processing them, and then displaying the result using various visual approaches give more detailed knowledge of understanding a disease state. The visual presentation performs multiple alignments and registration techniques using the complex and multi-dimensional images. The arrangement and registration of complex medical images having sparse data and control flow is a hard process. A medical imaging machine (e.g. radiological imaging) consumes 75% of processing time while aligning and registering, whereas a CT scan aligns images having a sample space of three-dimension with the reasonable isotropic resolution. These complex imaging applications have to follow anatomical and pathological structures while performing image acquisition, which demands efficient high-performance imaging hardware.
\nMedical imaging system uses different processing hardware such as reduced instruction set computer (RISC), application-specific instruction set processor (ASIP), single instruction multiple data (SIMD) processor, graphical processing units (GPUs), and field programmable gate arrays (FPGAs) [1, 2]. GPUs architecture uses advance vector processor architecture with dedicated memory and multiple stream multiprocessor (SM) having SIMD. The processing cores perform floating point operations. This high-end computing capability allows medical imaging applications to render complex medical images. In the past years, the GPU programming tools are grown and become competent in solving complex medical algorithm. On the other side, the performance of GPUs processing cores also increased, which allows medical imaging applications to give better results while diagnosing a problem.
\nMedical imaging is consistently held to be one of the most important advances in the history of medicine and has become an integral part of the diagnosis and treatment of patients around the globe. The medical statistic [3, 4] confirms that the early stage disease prediction, for example, breast, colorectal and lung cancers, etc., can save lives. This demands an improvement in diagnosis of the disease and screening techniques that generate high class, multidimensional images. With the development of medical imaging technology, the complexity of images also increased. It needs a high-performance computing architecture for real environment application processing. Existing medical imaging processing architectures face different issues and limitations related to hardware and software. Therefore, an efficient, scalable, and easily programmable high-performance medical imaging hardware architecture is required to solve complex imaging problems.
\nIn this chapter, we proposed a high-performance medical imaging system (HPMIS) for medical applications. The proposed system works as a standalone device that processes images taken from different medical imaging equipment in real time. The HPMIS architecture is a standalone heterogeneous system, which can perform image registration, storage, and processing in real-time environment. A software programming model is also proposed, which facilitates the medical scientists to write their imaging application without going into details of hardware. The proposed system is efficient in terms of performance and consumes low power due to the best utilization of hardware-software approach.
\nImaging applications for clinical methods and pathology study need high performance and efficiency. Several image processing environments and processing architectures exist for the medical imaging application, but to the best of our information, a programmable and high-performance scalable processing system is required for medical imaging applications.
\nIbanez et al. [5] designed an open-source medical imaging toolkit called the Insight Toolkit (ITK). The developed toolkit supports a number of platforms and gives programmers with an extended C++ framework of software tools for image understanding.
\nSchroeder et al. [6] developed an open-source Visualization ToolKit (VTK) for three-dimensional processing and visualization. VTK toolkit assists C++ libraries and algorithms for different parallel processing technologies and provides an interface to integrate with different databases.
\nWolf et al. [7] introduced Medical Imaging Interaction Toolkit (MITK) that assists to program medical image-based clinical software. The MITK gives an application programming framework that links with the ITK and the VTK libraries. The proposed HPMIS provides a medical imaging programming model, which supports data registration, memory management, and processing in hardware, and is easy to program. The HPMIS programming model is scalable for different architectures having reduced instruction set computing (RISC) multi-processors, application specific hardware cores, and vector processors.
\nBakalash et al. [8] suggested the MediCube system for 3D medical imaging. The system gives the reconstruction and visualization of three-dimension complex medical images. The processing system of the MediCube uses a RISC processor that performs the parallel processing for real-time voxel representation, whereas the HPMIS handles 3D medical imaging using a specialized local memory system and uses reconfigurable processing cores to process the incoming data. Bluetechnix [9] Black camera boards use specialized DSP processors and provide excellent image processing abilities at the expense of power, price, and complexity. Lee et al. [10] proposed a SONY DSP processing-based system.
\nJinghong et al. [11] proposed image processing system having heterogeneous (DSP and FPGA) processing cores. The HPMIS uses FPGA accelerators for high-speed data acquisition from single or multiple sensors. The HPMIS processing architecture uses multi-processor core architecture, each core using 32-bit RISC instruction set architecture (ISA) is integrated with the design for programmability, average performance, and low-cost systems.
\nPratx et al. [12] proposed a method for processing line-projection tasks to process the PET image reconstruction. The proposed method uses Nvidia processing cores and the CUDA programming model. Owens et al. [13] addressed the implementation of RISC processors on GPU cores-based processing system architecture. The authors showed the value of GPU for the tremendous compute capacity that reproduces the CT images and presents them on screen.
\nJiang et al. [14] suggested processing of 3D discrete transformation using hardware accelerator. The proposed system decreases the size of the accelerators lookup tables. The accelerators are developed in hardware description language and examined on Xilinx Virtex-E FPGA board.
\nCoric et al. [15] displayed a CT-based parallel beam back-projection algorithm and tested on FPGA-based hardware architecture. The FPGA-based system obtains the speedups up to 100× against the software running on a 1 GHz Pentium.
\nTassadaq et al. proposed programmable graphics controller [16, 17] for low-cost and low-power graphics system. The system takes two-dimensional images to process applications. Tassadaq et al. [18] also have proposed a visual processing system called ViPS for medical applications. The ViPS uses multiple RISC, vector processor, and multiple FPGA-based hardware accelerators. The ViPS gives a high performance by using advanced hardware architectural support such as registration system, memory system, and processing system.
\nImaging has become an essential tool in modern medicine science. Numerous powerful platforms to register, store, analyze, and process medical imaging applications have appeared in recent years. However, these systems are developed for a specific imaging application problem [19, 20, 21] and do not support generalized problems [22], which includes an indigenous system that could be customized according to the local needs. Moreover, the cost of such systems is on higher side that contributes to the increase in expenses of each scan. This results in discouraging patients, who could not afford these high costs and therefore compromise on their health. Also, in many medical imaging systems such as MRI, temporal resolution has been reported to be a major concern, which causes discomfort to patients with severe illness/pain as they have to be motionless for a longer period of time. In this chapter, a high-performance medical imaging system (HPMIS) is proposed that registers stores and processes complex and multi-dimensional medical imaging application in real-time. The high-performance medical imaging system provides a user-friendly programming environment and high-performance architecture to perform imaging data acquisition, registration, storage, analysis, and performs segmentation, filtering, and recognition for complex real-time complex and multidimensional medical images or videos. The proposed system is highly reliable concerning cost, performance, and power. High-performance medical imaging system architecture (shown in Figure 1) is implemented and tested on advanced heterogeneous multi-core and GPU-based systems.
\nHigh-performance medical imaging system.
High-performance specialized medical memory.
The proposed high-performance medical imaging system (HPMIS) has five sub systems: registration system, memory system, processing system, programming toolkit, and test applications.
\nThe registration system (RS) deals with a number of medical imaging devices with various interfaces. The RS supports multidimensional and scattered graphics data. The RS manages X-ray radiography, ultrasonic images, etc. and complex images such as MRI, CT, etc. The RS utilizes a RISC core and FPGA accelerator to access data from medical imaging devices. The RISC core is employed to obtain medical images having a complex structure, whereas the FPGA core is used to gather data having fixed data flow patterns. The registration system aligns images reasonably isotropic resolution and do not distort or deform the anatomical and pathological structures of images. The system is designed for navigation and visualization of multimodality and multidimensional images for 2D/3D, 4D Cardiac-CT and 5D Cardiac-PET-CT displays. The registration system supports all DICOM Files (mono-frame, multi-frames), the MRI/CT multi-frame format, JPEG Lossy, Lossless, LS and 2000, RLE, Monochrome1, Monochrome2, RGB, YBR, Planar and Palettes. The system supports 32-bit pixel resolution and is directly linked with memory system.
\nThe HPMIS memory system uses three types of memories: the program memory, the specialized medical memory, and the main memory. The HPMIS program memory utilizes the memory access descriptors [APMC][PPMC][PMC] that contain the knowledge of imaging data registration, data transfers, storage device, and processing cores. The descriptors provide the medical imaging programmer to choose a processing core for image processing and explain the complicated image structure.
\nThe HPMIS high-performance specialized imaging memory (shown in Figure 2) is directly connected to the processing system and provides single cycle data access. The high-performance specialized imaging memory holds the complex medical image data pattern to speed up the data transfer accesses. The high-performance specialized imaging memory has parameterizable and programmable 3D memory structure. Depending upon the available on-chip memory, the high-performance specialized medical memory can be organized into multiple frames. To exploit parallelism better, the frames of high-performance specialized medical memory are arranged physically into a multi-dimensional (1D/2D/3D) architecture to map the kernel access pattern on the high-performance specialized imaging memory.
\nThe slowest type of memory in the HPMIS architecture is main memory and is accessible by the whole system. The main memory has SDRAM, SD/SDHC memories, etc. interfaces to read/write data.
\nThe permanent memory of high-performance medical imaging system is used to store dense and large size of medical images in a nonvolatile memory permanently.
\nThe processing system is used to perform image processing on images stored in memory system. The medical application-specific hardware accelerator, vector processor, and the multi RISC processor cores are used in the HPMIS processing system.
\nThe HPMIS Medical Application Programming Model (MAPM) helps medical application programmers to develop applications without acknowledging hardware arrangements and details. The MAPM uses image processing functions for image segmentation, reconstruction, features extraction, and computation; and it also provides memory management and registration task and separate task for visualization. The MAPM presents various function calls, which include medical imaging flow, control, and processing. The MAPM data transfer tasks help disordered, random, strider 1D, 2D, 3D, and automated blocking for image/video transfer processes and move data between the medical imaging devices and the HPMIS memory system. The HPMIS MAPM intelligently pipeline, overlap, and parallelize image processing tasks based on hardware processing and memory resources. Table 1 presents functions that are used to develop the applications for the HPMIS architecture. The HPMIS incorporates 100 function calls for medical applications. The application calls are written in C/C++. The function allows the programmer to execute the function on RISC or Vector processors.
\nC/C++ device drivers to program/operate HPMIS.
To validate the HPMIS system, different medical applications are executed. The medical applications includes segmentation, K-mean clustering, object detection, object recognition, features extraction, 3D-Stereo filtering, ultrasonic image reconstruction, time domain filtering, frequency domain filtering, alpha blending, and Iris-based disease recognition.
\nThe high-performance medical imaging system is tested with the test applications discussed in Section 3.5. The results show that HPMIS-based architecture draws low power and gives high performance for the test applications. While executing test application on multi-RISC and FPGA cores of HPMIS, the results show that the HPMIS FPGA-cores improve the application performance 2.4 times compared to multi-core RISC system. The HPMIS performs real-time complex images reconstruction at higher rate.
\nIn this chapter, we have introduced and developed a high-performance medical imaging system (HPMIS) for medical imaging applications. The HPMIS has the capability to register multi-dimensional, complex, and high-resolution images, and processes the application on different heterogeneous multi-core processors. The HPMIS system gives efficient imaging access from multiple medical equipment. The HPMIS provides a medical programming environment that includes different function calls for the development of medical imaging application. The programming environment provides the data structure to present medical images or videos.
\nIn the past, Japanese houses were ridiculed as being “rabbit hutches” as they were smaller in scale, lower in quality, and shorter in average service life than those of Western countries, and examples were often given illustrating Japan as having the worst residential environment among major advanced countries. However, after the period of high economic growth since the chaotic postwar period, this environment has already greatly improved. In recent years high-performance housing stock has accumulated, and housing with functions not found in other countries have become common.
\nNeedless to say, when attempting an analysis of a housing market, it is necessary to fully understand the characteristics of the country. Below, we set out the reasons that have led to the false perception of Japanese housing still belonging to the era when they were ridiculed as rabbit hutches.
\nAlthough commonalities can be found in many parts of the Japanese housing market in comparison with the European and US housing markets, the following heterogeneity is conceivable as the postwar historical origin is different. It is possible that these are the cause of many misunderstandings.
\nIn Japan, many houses were destroyed due to the large-scale air raids during the Second World War, not only in metropolitan areas but also in regional cities. In particular, a large number of houses were destroyed in the Tokyo metropolitan area,1 and very-low-quality houses were built in a disorganized manner to satisfy the urgent housing demand in the chaotic postwar period. In the so-called high economic growth period that began in the mid-1950s, such houses were rapidly upgraded as large numbers of apartment buildings came up throughout Japan.2 In addition, the drastic change in Japanese lifestyle through the rapid economic growth triggered the renewal of old housing stock by Westernizing the traditional housing style of Japan.
\nThe private sector led construction to realize such a large-scale housing supply because the public sector was saddled with the large financial burden of postwar reconstruction. In particular, the government established a personal loan system to promote housing investment by households, and as a result, the ownership rate in postwar Japan significantly increased in comparison to before the war. Furthermore, as the supply of public housing was limited, a dedicated housing market for single-person renters formed in the rental housing market, which was rarely seen in Europe and the United States.
\nAs a result of these short-term housing renewals, Japanese housing was brought into a state where their style, quality, and housing equipment were greatly different depending on the period of construction. In addition, due to natural disasters such as the Great Hanshin earthquake3 and the Great East Japan earthquake,4 housing earthquake resistance and other legal regulations have been successively strengthened, thereby rapidly increasing the performance requirements of housing.5\n
\nThis history is also closely related to the shortness of service life, which is a characteristic of the previously ridiculed Japanese houses. Several reasons can be envisaged to explain the short service life of Japanese houses, but the two most influential factors are considered to be the urgent task of promoting the renovation of low-quality housing stock that was built to temporarily compensate for the housing shortage after the war and the fact that the stock renewal was promoted by strengthening public regulations due to large-scale earthquakes and other disasters.
\nIn addition, the high urban renewal rate can also be cited as a reason. In the rapid economic growth of postwar Japan, the main industrial structure shifted from primary to secondary industry in a single stroke, and urbanization was promoted throughout the country in the 1970s by developing highway and railway networks across the country, known as “Japanese archipelago remodeling.” In the 1980s, a policy was developed to transform the industrial structure, which was centered on secondary industry to tertiary industry. The transformation of Tokyo into an international financial center was a symbolic policy, and against this background, redevelopment rapidly advanced in major cities. Under such circumstances, the conversion of building use of even physically usable housing into offices, commercial facilities, and so on was promoted through rebuilding, and the advancement of land use was promoted [1].
\nAs a result, it can be said that the average service life of housing seen throughout the stock as a whole has been shortened over a long period.
\nIn addition to these features, it should be noted that the speed of technological innovation in Japanese housing is fast. “Technological innovation” here refers not only to the improvement of productivity on the manufacturing side but also the significant improvement of household welfare levels through the release of new products developed by R&D. In recent years, smart houses utilizing IOT and so on have become symbolic of advancing technology, but functions such as TV intercoms, bathroom dryers, system kitchens, and toilets with washlets, which are not often seen in European and American houses, have become common functions in Japanese houses and have greatly improved household living standards.
\nHowever, in a market where products with such new features arrive so quickly, the speed of obsolescence also increases. In these markets, when a new product appears, the old product is ordered to be withdrawn from the market, or its commodity value is greatly depreciated, that is, the service life of products is shortened.
\nThis study aims to measure the economic value of the functions of housing with new quality in the rental housing market in Tokyo, where technological advancement has been the greatest, and to clarify how much economic depreciation is occurring due to obsolescence. In Section 2, we present the model and the framework for empirical analysis together with the data, and in Section 3, we present the estimated results. According to the obtained results, new functions are being added sequentially to Japanese rental housing according to the age of the building, and these functions are non-negligible in the determination of housing rent, even when compared with location (LC) and the building structure (ST). The effect of obsolescence due to the addition of new functions was roughly—5%. In Section 4, we summarize the results by way of a conclusion.
\nA technique known as the hedonic approach is effective to decompose prices of commodities corresponding to different qualities. Since the hedonic approach theoretically depicts the behavior of suppliers and consumers in a market with diverse quality and presents a framework for empirical analysis, it is possible to approximately measure the household limit shadow price for new functions and to identify economic deterioration accompanying obsolescence [2, 3].
\nGenerally, the construction of household selection models in the residential market faces many difficulties compared to regular commodities and service markets. Not only is consumption expenditure high for housing, but consumers are also troubled by its highly nonuniform nature. Even when located in the same place, prices vary depending on the quality of housing, and even if the buildings are of the same quality, prices vary depending on the location. Furthermore, since housing has durability, depreciation has to be taken into consideration.
\nIn such a market, the law of one price assumed by traditional pricing theory cannot be applied. Furthermore, the application of a model that deals with differentiated products as analyzed by Lancaster [4] is not effective both theoretically and in empirical analysis. Accordingly, Rosen [5] theoretically clarified the type of market mechanism that is generated when commodity price data are a bundle of such attributes. Specifically, the relationship between the offer function of the commodity supplier, the bid function of the commodity consumer, and the market price function established by the equilibrium of these is closely examined, and the market price is characterized from the behavior of consumers and producers [6].
\nHowever, when attempting to estimate the hedonic function, we face various difficulties. The first is the existence of unobservable variables, the so-called omitted variable bias problem [7]. In general, in the estimation of a hedonic function for the housing market, only the location and building attributes, which are easy to obtain, are taken into consideration. However, it is also easy to envisage that the actual market price of housing will change depending on the environment surrounding the house and the various kinds of performance of the building.
\nAs for variable selection in the estimation of hedonic functions in Tokyo’s rental housing market, Nishi et al. [8] have conducted an exhaustive survey of the previous research. Nishi et al. [8] pointed that the housing amenities used in hedonic analyses are categorized as “location or accessibility,” “housing features,” and “site advantages.” This paper is focused on housing features, because they can be reflected in the rent due to the technological progress in the information systems and supply management.
\nAccessibility is defined as the “main accessibility related to commuting.” Housing features are defined as “amenities that can be changed by landlords.” Accessibility and housing features are the basic characteristics used as explanatory variables in hedonic models and are widely used in previous studies [2, 6, 9, 10].
\nSite advantages have also been researched recently using variables calculated using geographical information system or GIS [11, 12]. Shimizu [11] demonstrates that the environment surrounding housing is important in the case where house prices are determined by building use conditions and the characteristics of neighboring residents and suggests that in the case such variables are not taken into consideration and the estimated statistics of the hedonic function lack robustness. Nishi et al. [8] also show that there is a similar problem with estimated statistics when housing equipment is not taken into consideration. Fuerst and Shimizu [13] show that in the new condominium market in Tokyo, the offered value for highly novel functionality such as environmental performance differs greatly when taking the household’s annual income into account.
\nAs can be understood from these facts, attention must be given to the kind of variables that are used in estimating the hedonic function. According to Nelson [14], housing characteristics to be considered in function estimation are classified as follows.
\nExcluding characteristics related to traffic convenience, Nelson [14] considers it possible to categorize the positional characteristics of housing into housing equipment and location. Of these, “housing equipment” is variable and depends on the owner of the house, and “location” is an element that cannot be changed. Meanwhile, Chau and Chin [15] and Shimizu [11] add neighborhood characteristics.
\nIn addition to location and building structure, this study classifies housing equipment into equipment ancillary to the room (room equipment (RE)) and to the building (building equipment (BE)) and also takes the conditions of the contract into account to estimate their marginal price effect by estimating the hedonic function and to estimate the extent of obsolescence caused by the appearance of products with new performance.
\nSince the latter half of the 1990s, the use of the Internet for real estate advertising has expanded rapidly in Japan, and websites dealing with a large amount of rental advert data have been developed. This study uses the data on homes managed by LIFULL Co., Ltd. which is a major real estate portal site.6\n
\nMultiple real estate companies post concurrent advertisements for the same property on the real estate website, so we eliminated the duplicates from the data by grouping them by the criteria of address, property name, and room number.7 Furthermore, we independently assigned daytime railway travel time from Tokyo station to the nearest station to the property (minutes), which was not included in the data posted on the portal.
\nSince the aim of this study is to identify the effect due to the addition of new functions over time, we hypothesize that the price structure will change according to the period of construction.8 Data were segmented into the following three stocks:
Old stock: built between 1968 and 1981
Main stock: built between 1982 and 1999
New stock: built between 2000 and 2018
Earthquake resistance standards were greatly revised in 1981 according to the Building Standards Act, which stipulates building quality in Japan, and the earthquake resistance of buildings differs greatly according to whether they were built in or before 1981 or in or after 1982; buildings were therefore categorized using 1981 as a basis. There was also a major change in earthquake resistance standards in 2000, so this was also used as the standard for a category. In addition, the data used are for the 23 wards of Tokyo where a large volume of housing stocks was supplied due to a large population inflow.
\nAs a result of this process, 53,550 data points were acquired as data for analysis.9\n
\nA general hedonic model can be expressed as
\nwhere y is the explanatory variable, p is the housing rent, xi\n is the explanatory variable (housing characteristic), and βi\n and α are the estimation parameters.
\nIn this study, in addition to the commonly used location and building structure, housing equipment was added to the estimation of the hedonic function for the housing market. Specifically, we classified housing equipment into equipment ancillary to the room (room equipment) and equipment ancillary to the building (building equipment) and took the conditions of contract (CC) into account to construct a hedonic function.
\n\nEq. (1) can be rewritten as follows:
\nHere, the actual estimation formula can be expressed as follows:
\nIn Eq. (1), ln pit\n represents the log rent for i property at time t (October 2018). \n
Before analysis, we calculated the descriptive statistics of the data to be analyzed (\nTable 1\n). From the descriptive statistics, there are several features as follows, depending on the period of construction:
There is little difference between old and main stocks in average rent, but it is about 20% higher for new stock than the old stock.
There is no significant difference in the average floor area, the number of minutes by foot from the nearest station, and the number of minutes by train from Tokyo station.
Concerning the years since construction, the average of the total is 18.5 years and the standard deviation is 12.7 years, and the average value and standard deviation by construction date are consistent with the classification.
Variable | \nType | \nMean | \nStd. dev. | \nMin. | \nMax. | \n
---|---|---|---|---|---|
Monthly rent | \nAll | \n94,779 | \n34,873 | \n30,000 | \n249,000 | \n
(JYE) | \nOld stock | \n84,968 | \n30,631 | \n30,000 | \n240,000 | \n
\n | Main stock | \n85,305 | \n32,566 | \n30,000 | \n249,000 | \n
\n | New stock | \n103,040 | \n34,994 | \n45,000 | \n249,000 | \n
\n | New/old | \n121.3% | \n114.2% | \n150.0% | \n103.8% | \n
Floor space | \nAll | \n31.3 | \n13.4 | \n15.0 | \n100.0 | \n
(m2) | \nOld stock | \n32.5 | \n12.4 | \n15.0 | \n91.4 | \n
\n | Main stock | \n32.0 | \n15.3 | \n15.0 | \n100.0 | \n
\n | New stock | \n30.6 | \n12.1 | \n15.0 | \n99.5 | \n
\n | New/old | \n94.3% | \n97.7% | \n100.0% | \n108.9% | \n
Monthly rent/m3\n | \nAll | \n3192 | \n806 | \n988 | \n8134 | \n
(JYE) | \nOld stock | \n2717 | \n668 | \n1076 | \n6528 | \n
\n | Main stock | \n2864 | \n701 | \n988 | \n7535 | \n
\n | New stock | \n3501 | \n766 | \n1165 | \n8134 | \n
\n | New/old | \n128.9% | \n114.8% | \n108.3% | \n124.6% | \n
Age of unit | \nAll | \n18.5 | \n12.7 | \n0.0 | \n50.0 | \n
(year) | \nOld stock | \n42.3 | \n3.8 | \n37.0 | \n50.0 | \n
\n | Main stock | \n27.4 | \n4.4 | \n19.0 | \n36.0 | \n
\n | New stock | \n8.3 | \n5.5 | \n0.0 | \n18.0 | \n
\n | New/old | \n19.7% | \n144.0% | \n0.0% | \n36.0% | \n
Time to the nearest station | \nAll | \n7.7 | \n4.6 | \n0.6 | \n41.0 | \n
(minutes) | \nOld stock | \n7.4 | \n4.4 | \n1.0 | \n41.0 | \n
\n | Main stock | \n8.1 | \n4.8 | \n1.0 | \n40.0 | \n
\n | New stock | \n7.4 | \n4.4 | \n0.6 | \n38.0 | \n
\n | New/old | \n99.4% | \n98.8% | \n62.5% | \n92.7% | \n
Time to Tokyo station | \nAll | \n27.2 | \n8.6 | \n1.0 | \n48.0 | \n
(minutes) | \nOld stock | \n26.5 | \n8.3 | \n4.0 | \n48.0 | \n
\n | Main stock | \n28.8 | \n8.1 | \n4.0 | \n48.0 | \n
\n | New stock | \n26.2 | \n8.8 | \n1.0 | \n48.0 | \n
\n | New/old | \n99.1% | \n105.4% | \n25.0% | \n100.0% | \n
Number of observations (all) = 53,550 | \n
Descriptive statistics.
Based on these features, there is found to be little difference between the physical space distribution due to the period of construction and only the building quality changes.
\nNext, we examined the distribution of old/main/new stock for each of the 23 wards (\nTable 2\n). The ratio of new stock ratio exceeds 70% in Chiyoda, Chuo, and Minato wards, which make up the center of Tokyo. As previously mentioned, the probability of large-scale redevelopment and so on being carried out increases for more urban areas, which may have caused this result due to active stock renewal.10\n
\n\n | Number of observations | \nRatio | \n|||||||
---|---|---|---|---|---|---|---|---|---|
Item | \nTotal | \nOld stock | \nMain stock | \nNew stock | \nTotal | \nOld stock | \nMain stock | \nNew stock | \nNew-old | \n
\nRoom equipment\n | \n|||||||||
Air conditioning | \n49.088 | \n4029 | \n17.883 | \n27.176 | \n91.7% | \n82.8% | \n89.5% | \n94.7% | \n11.9% | \n
Hot water supply | \n44.841 | \n3879 | \n16.961 | \n24.001 | \n83.7% | \n79.7% | \n84.9% | \n83.6% | \n3.9% | \n
Indoor WM area | \n43.954 | \n2663 | \n14.696 | \n26.595 | \n82.1% | \n54.7% | \n73.5% | \n92.7% | \n38.0% | \n
Separate bath and toilet | \n43.943 | \n3447 | \n12.851 | \n27.645 | \n82.1% | \n70.8% | \n64.3% | \n96.3% | \n25.5% | \n
Flooring | \n43.269 | \n3364 | \n14.915 | \n24.990 | \n80.8% | \n69.1% | \n74.6% | \n87.1% | \n18.0% | \n
Balcony | \n40.851 | \n3204 | \n15.276 | \n22.371 | \n76.3% | \n65.8% | \n76.4% | \n77.9% | \n12.1% | \n
System kitchen | \n27.758 | \n1093 | \n5666 | \n20.999 | \n51.8% | \n22.5% | \n28.4% | \n73.2% | \n50.7% | \n
Separate washroom | \n26.292 | \n1412 | \n6221 | \n18.659 | \n49.1% | \n29.0% | \n31.1% | \n65.0% | \n36.0% | \n
1 gas stove | \n25.300 | \n1416 | \n6396 | \n17.488 | \n47.2% | \n29.1% | \n32.0% | \n60.9% | \n31.8% | \n
Washlet | \n23.221 | \n1089 | \n3265 | \n18.867 | \n43.4% | \n22.4% | \n16.3% | \n65.7% | \n43.4% | \n
Bathroom dryer | \n20.322 | \n186 | \n1077 | \n19.059 | \n37.9% | \n3.8% | \n5.4% | \n66.4% | \n62.6% | \n
2 gas stoves | \n18.632 | \n1081 | \n3304 | \n14.247 | \n34.8% | \n22.2% | \n16.5% | \n49.6% | \n27.4% | \n
Reheating bath | \n15.127 | \n1268 | \n3459 | \n10.400 | \n28.2% | \n26.0% | \n17.3% | \n36.2% | \n10.2% | \n
Washroom with shower | \n12.678 | \n364 | \n1764 | \n10.550 | \n23.7% | \n7.5% | \n8.8% | \n36.8% | \n29.3% | \n
Own house rental | \n7187 | \n497 | \n1588 | \n5102 | \n13.4% | \n10.2% | \n7.9% | \n17.8% | \n7.6% | \n
IH stovetop | \n6623 | \n215 | \n2653 | \n3755 | \n12.4% | \n4.4% | \n13.3% | \n13.1% | \n8.7% | \n
Walk-in closet | \n3694 | \n88 | \n235 | \n3371 | \n6.9% | \n1.8% | \n1.2% | \n11.7% | \n9.9% | \n
Counter kitchen | \n3409 | \n70 | \n516 | \n2823 | \n6.4% | \n1.4% | \n2.6% | \n9.8% | \n8.4% | \n
With loft | \n2110 | \n19 | \n754 | \n1337 | \n3.9% | \n0.4% | \n3.8% | \n4.7% | \n4.3% | \n
Underfloor heating | \n1147 | \n8 | \n87 | \n1052 | \n2.1% | \n0.2% | \n0.4% | \n3.7% | \n3.5% | \n
\nBuilding equipment\n | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
Bicycle parking lot | \n33.795 | \n2096 | \n11.385 | \n20.314 | \n63.1% | \n43.1% | \n57.0% | \n70.8% | \n27.7% | \n
Fiber optic Internet | \n27.307 | \n2085 | \n10.056 | \n15.166 | \n51.0% | \n42.8% | \n50.3% | \n52.8% | \n10.0% | \n
TV intercom | \n26.689 | \n953 | \n4232 | \n21.504 | \n49.8% | \n19.6% | \n21.2% | \n74.9% | \n55.4% | \n
Automatic entrance door | \n26.042 | \n337 | \n5062 | \n20.643 | \n48.6% | \n6.9% | \n25.3% | \n71.9% | \n65.0% | \n
Cable TV | \n23.211 | \n1316 | \n8314 | \n13.581 | \n43.3% | \n27.0% | \n41.6% | \n47.3% | \n20.3% | \n
BS antenna | \n20.013 | \n472 | \n4430 | \n15.111 | \n37.4% | \n9.7% | \n22.2% | \n52.7% | \n43.0% | \n
Elevator | \n19.587 | \n1189 | \n5387 | \n13.011 | \n36.6% | \n24.4% | \n27.0% | \n45.3% | \n20.9% | \n
Tiling wall | \n15.751 | \n561 | \n5265 | \n9925 | \n29.4% | \n11.5% | \n26.3% | \n34.6% | \n23.1% | \n
Delivery locker | \n15.163 | \n119 | \n1550 | \n13.494 | \n28.3% | \n2.4% | \n7.8% | \n47.0% | \n44.6% | \n
Security camera | \n12.694 | \n302 | \n1849 | \n10.543 | \n23.7% | \n6.2% | \n9.3% | \n36.7% | \n30.5% | \n
CS antenna | \n11.888 | \n304 | \n1837 | \n9747 | \n22.2% | \n6.2% | \n9.2% | \n34.0% | \n27.7% | \n
Garbage 24H available | \n6670 | \n130 | \n728 | \n5812 | \n12.5% | \n2.7% | \n3.6% | \n20.3% | \n17.6% | \n
Bike parking lot | \n6335 | \n354 | \n1875 | \n4106 | \n11.8% | \n7.3% | \n9.4% | \n14.3% | \n7.0% | \n
Design by artist | \n4068 | \n29 | \n286 | \n3753 | \n7.6% | \n0.6% | \n1.4% | \n13.1% | \n12.5% | \n
Seismic structure | \n3827 | \n37 | \n702 | \n3088 | \n7.1% | \n0.8% | \n3.5% | \n10.8% | \n10.0% | \n
\nContract conditions\n | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
with NO guarantor | \n20.257 | \n1214 | \n6274 | \n12.769 | \n37.8% | \n24.9% | \n31.4% | \n44.5% | \n19.6% | \n
No pets | \n8417 | \n733 | \n3540 | \n4144 | \n15.7% | \n15.1% | \n17.7% | \n14.4% | \n-0.6% | \n
NO musical instrument | \n6704 | \n605 | \n2702 | \n3397 | \n12.5% | \n12.4% | \n13.5% | \n11.8% | \n-0.6% | \n
NO office use | \n5253 | \n297 | \n1731 | \n3225 | \n9.8% | \n6.1% | \n8.7% | \n11.2% | \n5.1% | \n
FREE Internet | \n4682 | \n100 | \n616 | \n3966 | \n8.7% | \n2.1% | \n3.1% | \n13.8% | \n11.8% | \n
Pet consultation | \n3906 | \n210 | \n801 | \n2895 | \n7.3% | \n4.3% | \n4.0% | \n10.1% | \n5.8% | \n
Pets allowed | \n2189 | \n150 | \n437 | \n1602 | \n4.1% | \n3.1% | \n2.2% | \n5.6% | \n2.5% | \n
Contract with limited term | \n1673 | \n311 | \n511 | \n851 | \n3.1% | \n6.4% | \n2.6% | \n3.0% | \n-3.4% | \n
Office use allowed | \n1319 | \n362 | \n508 | \n449 | \n2.5% | \n7.4% | \n2.5% | \n1.6% | \n-5.9% | \n
\nBuilding structure\n | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
Wooden | \n10.851 | \n1285 | \n4273 | \n5293 | \n20.3% | \n26.4% | \n21.4% | \n18.4% | \n-8.0% | \n
Steel frame | \n13.796 | \n891 | \n6044 | \n6861 | \n25.8% | \n18.3% | \n30.2% | \n23.9% | \n5.6% | \n
RC | \n23.654 | \n2074 | \n7635 | \n13.945 | \n44.2% | \n42.6% | \n38.2% | \n48.6% | \n6.0% | \n
SRC | \n3644 | \n599 | \n1626 | \n1419 | \n6.8% | \n12.3% | \n8.1% | \n4.9% | \n-7.4% | \n
Others | \n1605 | \n19 | \n404 | \n1182 | \n3.0% | \n0.4% | \n2.0% | \n4.1% | \n3.7% | \n
\nOthers\n | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
High-rise block (16F over) | \n387 | \n3 | \n54 | \n330 | \n0.7% | \n0.1% | \n0.3% | \n1.1% | \n1.1% | \n
Room on the first floor | \n13.265 | \n894 | \n5217 | \n7154 | \n24.8% | \n18.4% | \n26.1% | \n24.9% | \n6.6% | \n
Distribution of equipment in old stock, main stock, and new stock.
Outside the three wards of the city center, the ratio of new stock is over 70% in Taito and Sumida wards and over 60% in Koto and Shinagawa wards, but this may be due to the supply of large-scale high-rise condominiums due to the relaxation of regulations in the 1990s. The ratio of new stock in other wards is around 50% (\nTable 3\n).
\nWard | \nOld stock | \nMain stock | \nNew stock | \nTotal | \nOld stock | \nMain stock | \nNew stock | \n
---|---|---|---|---|---|---|---|
Chiyoda | \n39 | \n65 | \n342 | \n446 | \n8.7% | \n14.6% | \n76.7% | \n
Chuo | \n57 | \n117 | \n740 | \n914 | \n6.2% | \n12.8% | \n81.0% | \n
Minato | \n137 | \n182 | \n927 | \n1246 | \n11.0% | \n14.6% | \n74.4% | \n
Shinjuku | \n284 | \n574 | \n1264 | \n2122 | \n13.4% | \n27.0% | \n59.6% | \n
Bunkyo | \n144 | \n379 | \n706 | \n1229 | \n11.7% | \n30.8% | \n57.4% | \n
Taito | \n86 | \n210 | \n796 | \n1092 | \n7.9% | \n19.2% | \n72.9% | \n
Sumida | \n103 | \n348 | \n1077 | \n1528 | \n6.7% | \n22.8% | \n70.5% | \n
Kouto | \n134 | \n454 | \n1056 | \n1644 | \n8.2% | \n27.6% | \n64.2% | \n
Shinagawa | \n190 | \n650 | \n1463 | \n2303 | \n8.3% | \n28.2% | \n63.5% | \n
Meguro | \n134 | \n537 | \n789 | \n1460 | \n9.2% | \n36.8% | \n54.0% | \n
Ota | \n458 | \n2022 | \n3054 | \n5534 | \n8.3% | \n36.5% | \n55.2% | \n
Setagaya | \n494 | \n2605 | \n2450 | \n5549 | \n8.9% | \n46.9% | \n44.2% | \n
Shibuya | \n188 | \n425 | \n908 | \n1521 | \n12.4% | \n27.9% | \n59.7% | \n
Nakano | \n292 | \n996 | \n1367 | \n2655 | \n11.0% | \n37.5% | \n51.5% | \n
Suginami | \n421 | \n1915 | \n1778 | \n4114 | \n10.2% | \n46.5% | \n43.2% | \n
Toshima | \n189 | \n687 | \n1019 | \n1895 | \n10.0% | \n36.3% | \n53.8% | \n
Kita | \n300 | \n797 | \n1061 | \n2158 | \n13.9% | \n36.9% | \n49.2% | \n
Arakawa | \n100 | \n339 | \n582 | \n1021 | \n9.8% | \n33.2% | \n57.0% | \n
Itabashi | \n291 | \n1254 | \n1441 | \n2986 | \n9.7% | \n42.0% | \n48.3% | \n
Nerima | \n243 | \n1639 | \n1796 | \n3678 | \n6.6% | \n44.6% | \n48.8% | \n
Adachi | \n182 | \n1020 | \n1518 | \n2720 | \n6.7% | \n37.5% | \n55.8% | \n
Katsushika | \n177 | \n926 | \n1074 | \n2177 | \n8.1% | \n42.5% | \n49.3% | \n
Edogawa | \n225 | \n1841 | \n1492 | \n3558 | \n6.3% | \n51.7% | \n41.9% | \n
Total | \n4868 | \n19,982 | \n28,700 | \n53,550 | \n9.1% | \n37.3% | \n53.6% | \n
Spatial distribution of rental housing.
\n\nTable 4\n shows the ancillary equipment rate by period of construction. Equipment was classified into that ancillary to the room, ancillary to the building, and conditions of contract.11\n
\nDependent variable | \nln (monthly rent) JPY | \n\n | \n | \n | \n | \n | \n | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
Estimation method | \n\n | OLS | \n|||||||||
Number of observations | \n\n | 53,520 | \n4867 | \n19,975 | \n28,678 | \n\n | |||||
Adj R-squared | \n\n | 0.894 | \n0.853 | \n0.897 | \n0.892 | \n\n | |||||
\nIndependent variables\n | \n\nMark\n | \n\nAll\n | \n\nOld stock\n | \n\nMain stock\n | \n\nNew stock\n | \nNew-old | \n|||||
\nCoef.\n | \n\nP>t\n | \n\nCoef.\n | \n\nP>t\n | \n\nCoef.\n | \n\nP>t\n | \n\nCoef.\n | \n\nP>t\n | \nCoef. | \n|||
Age of unit (year) | \n\n | −0.53% | \n0.00 | \n−0.13% | \n0.01 | \n−0.43% | \n0.00 | \n−0.63% | \n0.00 | \n−0.50% | \n|
Old stock dummy | \n\n | 1.53% | \n0.00 | \n(Omitted) | \n(Omitted) | \n(Omitted) | \n− | \n||||
Main stock dummy | \n−0.54% | \n0.01 | \n(Omitted) | \n(Omitted) | \n(Omitted) | \n− | \n|||||
New stock dummy | \nBaseline | \n(Omitted) | \n(Omitted) | \n(Omitted) | \n− | \n||||||
Floor space (m2) | \n\n | 1.69% | \n0.00 | \n1.60% | \n0.00 | \n1.61% | \n0.00 | \n1.73% | \n0.00 | \n0.13% | \n|
Time to Tokyo station (minutes) | \n−0.70% | \n0.00 | \n−0.67% | \n0.00 | \n−0.77% | \n0.00 | \n−0.64% | \n0.00 | \n0.03% | \n||
Time to the nearest station (minutes) | \n−0.62% | \n0.00 | \n−0.53% | \n0.00 | \n−0.61% | \n0.00 | \n−0.64% | \n0.00 | \n−0.11% | \n||
Building | \nWooden | \n\n | Baseline | \nBaseline | \nBaseline | \nBaseline | \n− | \n||||
Structure | \nSteel frame | \n4.28% | \n0.00 | \n7.22% | \n0.00 | \n3.67% | \n0.00 | \n3.51% | \n0.00 | \n−3.71% | \n|
\n | RC | \n\n | 9.26% | \n0.00 | \n13.04% | \n0.00 | \n8.41% | \n0.00 | \n8.17% | \n0.00 | \n−4.87% | \n
\n | SRC | \n\n | 10.75% | \n0.00 | \n13.10% | \n0.00 | \n9.72% | \n0.00 | \n8.99% | \n0.00 | \n−4.11% | \n
\n | Others | \n\n | 4.01% | \n0.00 | \n6.83% | \n0.11 | \n4.20% | \n0.00 | \n3.25% | \n0.00 | \n−3.58% | \n
Wards | \nChiyoda | \n\n | −1.44% | \n0.01 | \n8.72% | \n0.00 | \n3.21% | \n0.03 | \n−3.03% | \n0.00 | \n−11.75% | \n
\n | Chuo | \n\n | −3.77% | \n0.00 | \n0.14% | \n0.94 | \n−2.84% | \n0.01 | \n−4.29% | \n0.00 | \n−4.43% | \n
\n | Minato | \n\n | 12.01% | \n0.00 | \n17.08% | \n0.00 | \n11.20% | \n0.00 | \n10.75% | \n0.00 | \n−6.34% | \n
\n | Shinjuku | \n\n | 0.54% | \n0.07 | \n3.46% | \n0.00 | \n0.15% | \n0.77 | \n−0.54% | \n0.14 | \n−4.00% | \n
\n | Bunkyo | \n\n | −5.38% | \n0.00 | \n−4.39% | \n0.00 | \n−5.82% | \n0.00 | \n−5.52% | \n0.00 | \n−1.13% | \n
\n | Taito | \n\n | −14.43% | \n0.00 | \n−12.95% | \n0.00 | \n−14.68% | \n0.00 | \n−14.73% | \n0.00 | \n−1.78% | \n
\n | Sumida | \n\n | −15.60% | \n0.00 | \n−15.02% | \n0.00 | \n−14.04% | \n0.00 | \n−16.21% | \n0.00 | \n−1.19% | \n
\n | Koto | \n\n | −13.38% | \n0.00 | \n−12.53% | \n0.00 | \n−12.56% | \n0.00 | \n−13.80% | \n0.00 | \n−1.27% | \n
\n | Shinagawa | \n\n | −6.09% | \n0.00 | \n−2.83% | \n0.02 | \n−6.83% | \n0.00 | \n−6.62% | \n0.00 | \n−3.79% | \n
\n | Meguro | \n\n | 8.18% | \n0.00 | \n10.71% | \n0.00 | \n6.95% | \n0.00 | \n7.76% | \n0.00 | \n−2.95% | \n
\n | Ota | \n\n | −11.59% | \n0.00 | \n−9.71% | \n0.00 | \n−10.11% | \n0.00 | \n−13.14% | \n0.00 | \n−3.42% | \n
\n | Setagaya | \n\n | Baseline | \nBaseline | \nBaseline | \nBaseline | \n− | \n||||
\n | Shibuya | \n\n | 10.74% | \n0.00 | \n12.23% | \n0.00 | \n7.93% | \n0.00 | \n11.20% | \n0.00 | \n−1.03% | \n
\n | Nakano | \n\n | −4.79% | \n0.00 | \n−3.33% | \n0.00 | \n−3.76% | \n0.00 | \n−6.01% | \n0.00 | \n−2.68% | \n
\n | Suginami | \n\n | −5.20% | \n0.00 | \n−4.42% | \n0.00 | \n−4.99% | \n0.00 | \n−5.64% | \n0.00 | \n−1.22% | \n
\n | Toshima | \n\n | −7.34% | \n0.00 | \n−4.23% | \n0.00 | \n−6.35% | \n0.00 | \n−9.01% | \n0.00 | \n−4.79% | \n
\n | Kita | \n\n | −16.82% | \n0.00 | \n−14.43% | \n0.00 | \n−16.10% | \n0.00 | \n−17.69% | \n0.00 | \n−3.25% | \n
\n | Arakawa | \n\n | −19.82% | \n0.00 | \n−17.14% | \n0.00 | \n−18.83% | \n0.00 | \n−20.71% | \n0.00 | \n−3.57% | \n
\n | Itabashi | \n\n | −15.50% | \n0.00 | \n−15.73% | \n0.00 | \n−15.13% | \n0.00 | \n−15.86% | \n0.00 | \n−0.12% | \n
\n | Nerima | \n\n | −12.61% | \n0.00 | \n−10.86% | \n0.00 | \n−12.29% | \n0.00 | \n−12.95% | \n0.00 | \n−2.09% | \n
\n | Adachi | \n\n | −27.31% | \n0.00 | \n−24.71% | \n0.00 | \n−27.17% | \n0.00 | \n−27.90% | \n0.00 | \n−3.19% | \n
\n | Katsushika | \n\n | −26.27% | \n0.00 | \n−24.68% | \n0.00 | \n−26.83% | \n0.00 | \n−25.97% | \n0.00 | \n−1.29% | \n
\n | Edogawa | \n\n | −21.84% | \n0.00 | \n−19.56% | \n0.00 | \n−21.90% | \n0.00 | \n−21.92% | \n0.00 | \n−2.35% | \n
Difference between max. and min. | \n39.32% | \n\n | 41.79% | \n\n | 38.37% | \n\n | 39.10% | \n\n | −2.69% | \n
Results of hedonic equations: main estimated results.
Housing equipment items are arranged in descending order of ancillary rate in all samples, and a comparison is made between old, main, and new stocks.
\nIn terms of room equipment, the five items (i) air conditioning, (ii) hot water supply, (iii) indoor washing machine area, (iv) separate bath and toilet, and (v) flooring have a high ancillary rate of over 80%. The equipments for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) are bathroom dryer (+62.6%), system kitchen (+50.7%), toilet with washlet (+43.4%), indoor washing machine area (+38.0%), and separate washroom (+36.0%).
\nIn terms of building equipment, the ancillary rate is over 50% for the bicycle parking lot and fiber optic Internet. The equipment for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) is automatic entrance door (+65.0%), TV intercom (+55.4%), delivery locker (+44.6%), BS antenna (+43.0%), and security camera (+30.5%). In the conditions of contract, there are no items of note except for guarantor unnecessary, which is high at 37.8%, and only guarantor unnecessary (+19.6%) has a large difference in ancillary rate between old and new stocks (ancillary rate increased), but free Internet is also +11.8%.12\n
\nOverall, the rise in security equipment is significant in building equipment, and the rise in the equipment that improves the living convenience is significant in room equipment. In addition, the ratio of building structures also shows changes, such as wooden buildings decreasing by 8.0% and SRC by 7.4%, while steel frames increase by 5.6% and RC by 6.0%.13\n
\n\n\nTable 5\n shows the estimated results of the model. In addition, \nFigure 1\n illustrates the dummy partial regression coefficients for the equipment.
\nIndependent variables | \nAll | \nOld stock | \nMain stock | \nNew stock | \nNew-old | \n||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Coef. | \nP>t | \nCoef. | \nP>t | \nCoef. | \nP>t | \nCoef. | \nP>t | \nCoef. | \n|||
High-rise block (16F over) | \n8.74% | \n0.00 | \n14.12% | \n0.08 | \n4.35% | \n0.01 | \n9.22% | \n0.00 | \n−4.89% | \n||
Room on the first floor | \n−2.76% | \n0.00 | \n−0.55% | \n0.28 | \n−2.94% | \n0.00 | \n−3.00% | \n0.00 | \n−2.44% | \n||
RE | \nAir conditioning | \n\n | 0.82% | \n0.00 | \n1.87% | \n0.00 | \n0.18% | \n0.48 | \n0.02% | \n0.96 | \n−1.86% | \n
\n | Hot water supply | \n\n | −1.77% | \n0.00 | \n0.58% | \n0.24 | \n−1.03% | \n0.00 | \n−2.42% | \n0.00 | \n−3.01% | \n
\n | Indoor WM area | \n\n | 1.27% | \n0.00 | \n2.73% | \n0.00 | \n1.73% | \n0.00 | \n−0.77% | \n0.00 | \n−3.50% | \n
\n | Flooring | \n\nA\n | \n0.16% | \n0.22 | \n3.35% | \n0.00 | \n0.50% | \n0.01 | \n−1.81% | \n0.00 | \n−5.17% | \n
\n | Separate bath and toilet | \n\nA\n | \n5.07% | \n0.00 | \n5.58% | \n0.00 | \n6.46% | \n0.00 | \n1.55% | \n0.00 | \n−4.03% | \n
\n | balcony | \n\nA\n | \n0.84% | \n0.00 | \n3.28% | \n0.00 | \n1.82% | \n0.00 | \n0.01% | \n0.95 | \n−3.27% | \n
\n | System kitchen | \n\n | 1.85% | \n0.00 | \n4.62% | \n0.00 | \n2.40% | \n0.00 | \n0.79% | \n0.00 | \n−3.83% | \n
\n | Separate washroom | \n\n | 2.11% | \n0.00 | \n2.18% | \n0.00 | \n2.35% | \n0.00 | \n2.16% | \n0.00 | \n−0.03% | \n
\n | 1 gas stove | \n\n | −0.52% | \n0.00 | \n−1.09% | \n0.04 | \n0.13% | \n0.52 | \n−1.13% | \n0.00 | \n−0.05% | \n
\n | Washlet | \n\nA\n | \n2.20% | \n0.00 | \n3.17% | \n0.00 | \n2.75% | \n0.00 | \n1.16% | \n0.00 | \n−2.01% | \n
\n | Bathroom dryer | \n\nA\n | \n1.35% | \n0.00 | \n4.90% | \n0.00 | \n3.07% | \n0.00 | \n1.29% | \n0.00 | \n−3.61% | \n
\n | 2 gas stoves | \n\n | −0.34% | \n0.01 | \n−0.03% | \n0.95 | \n1.17% | \n0.00 | \n−0.09% | \n0.55 | \n−0.05% | \n
\n | Reheating bath | \n\nC\n | \n2.22% | \n0.00 | \n0.26% | \n0.56 | \n0.06% | \n0.82 | \n3.45% | \n0.00 | \n3.18% | \n
\n | Washroom with shower | \n\n | −1.16% | \n0.00 | \n0.64% | \n0.41 | \n−0.18% | \n0.55 | \n−1.33% | \n0.00 | \n−1.97% | \n
\n | Own house rental | \n\nD\n | \n−2.92% | \n0.00 | \n−0.54% | \n0.45 | \n−1.87% | \n0.00 | \n−3.42% | \n0.00 | \n−2.88% | \n
\n | IH stovetop | \n\nD\n | \n−1.03% | \n0.00 | \n−0.65% | \n0.49 | \n−1.68% | \n0.00 | \n−1.62% | \n0.00 | \n−0.97% | \n
\n | Walk-in closet | \n\nB\n | \n1.22% | \n0.00 | \n4.33% | \n0.00 | \n0.77% | \n0.29 | \n0.88% | \n0.00 | \n−3.45% | \n
\n | Counter kitchen | \n\n | 1.10% | \n0.00 | \n2.83% | \n0.08 | \n0.03% | \n0.95 | \n0.72% | \n0.00 | \n−2.11% | \n
\n | With loft | \n\n | 4.72% | \n0.00 | \n5.59% | \n0.07 | \n4.08% | \n0.00 | \n4.19% | \n0.00 | \n−1.40% | \n
\n | Underfloor heating | \n\nC\n | \n5.19% | \n0.00 | \n−1.55% | \n0.73 | \n−1.09% | \n0.36 | \n4.97% | \n0.00 | \n6.52% | \n
BE | \nBicycle parking lot | \n\n | −0.94% | \n0.00 | \n−0.71% | \n0.09 | \n−0.70% | \n0.00 | \n−0.96% | \n0.00 | \n−0.25% | \n
\n | Fiber optic Internet | \n\n | −1.04% | \n0.00 | \n−1.83% | \n0.00 | \n−0.93% | \n0.00 | \n−0.91% | \n0.00 | \n0.92% | \n
\n | TV intercom | \n\nA\n | \n1.08% | \n0.00 | \n3.99% | \n0.00 | \n1.71% | \n0.00 | \n−0.15% | \n0.34 | \n−4.14% | \n
\n | Automatic entrance door | \n\nA\n | \n1.74% | \n0.00 | \n4.47% | \n0.00 | \n2.72% | \n0.00 | \n1.63% | \n0.00 | \n−2.84% | \n
\n | Cable TV | \n\n | −0.63% | \n0.00 | \n−1.61% | \n0.00 | \n−0.26% | \n0.13 | \n−0.51% | \n0.00 | \n1.10% | \n
\n | BS antenna | \n\n | −1.25% | \n0.00 | \n−2.58% | \n0.01 | \n−0.05% | \n0.83 | \n−1.45% | \n0.00 | \n1.13% | \n
\n | Elevator | \n\n | 2.52% | \n0.00 | \n2.89% | \n0.00 | \n2.10% | \n0.00 | \n2.63% | \n0.00 | \n−0.26% | \n
\n | Tiling wall | \n\n | −1.44% | \n0.00 | \n−1.91% | \n0.00 | \n−1.21% | \n0.00 | \n−0.97% | \n0.00 | \n0.93% | \n
\n | Delivery locker | \n\nA\n | \n2.03% | \n0.00 | \n4.55% | \n0.00 | \n1.42% | \n0.00 | \n2.68% | \n0.00 | \n−1.87% | \n
\n | Security camera | \n\nC\n | \n1.33% | \n0.00 | \n0.62% | \n0.45 | \n1.06% | \n0.00 | \n1.61% | \n0.00 | \n0.99% | \n
\n | CS antenna | \n\n | 0.60% | \n0.00 | \n1.76% | \n0.15 | \n−0.69% | \n0.04 | \n1.25% | \n0.00 | \n−0.51% | \n
\n | Garbage 24H available | \n\nC\n | \n−0.13% | \n0.49 | \n−1.58% | \n0.18 | \n−0.84% | \n0.06 | \n0.98% | \n0.00 | \n2.56% | \n
\n | Bike parking lot | \n\nC\n | \n0.75% | \n0.00 | \n−0.38% | \n0.61 | \n0.29% | \n0.28 | \n0.94% | \n0.00 | \n1.32% | \n
\n | Design by artist | \n\n | 0.45% | \n0.02 | \n0.62% | \n0.80 | \n1.78% | \n0.01 | \n0.52% | \n0.01 | \n−0.09% | \n
\n | Seismic structure | \n\n | −2.25% | \n0.00 | \n−4.11% | \n0.05 | \n−1.95% | \n0.00 | \n−1.82% | \n0.00 | \n2.29% | \n
CC | \nwith NO guarantor | \n\nD\n | \n−0.82% | \n0.00 | \n−1.47% | \n0.00 | \n−1.07% | \n0.00 | \n−0.23% | \n0.08 | \n1.24% | \n
\n | No pets | \n\n | 0.06% | \n0.77 | \n−1.46% | \n0.12 | \n0.37% | \n0.25 | \n0.47% | \n0.07 | \n1.93% | \n
\n | Pet consultation | \n\n | 3.24% | \n0.00 | \n2.85% | \n0.00 | \n4.13% | \n0.00 | \n3.10% | \n0.00 | \n0.25% | \n
\n | Pets allowed | \n\n | 2.57% | \n0.00 | \n2.17% | \n0.05 | \n3.40% | \n0.00 | \n2.35% | \n0.00 | \n0.18% | \n
\n | No musical instrument | \n\n | −0.32% | \n0.15 | \n1.61% | \n0.11 | \n0.22% | \n0.54 | \n−0.83% | \n0.00 | \n−2.44% | \n
\n | No office use | \n\n | 0.81% | \n0.00 | \n−1.18% | \n0.18 | \n0.01% | \n0.97 | \n1.13% | \n0.00 | \n2.31% | \n
\n | Office use allowed | \n\nC\n | \n5.04% | \n0.00 | \n2.34% | \n0.00 | \n4.01% | \n0.00 | \n6.11% | \n0.00 | \n3.77% | \n
\n | Free Internet | \n\nB\n | \n0.82% | \n0.00 | \n3.01% | \n0.02 | \n0.58% | \n0.19 | \n0.68% | \n0.00 | \n−2.33% | \n
\n | Contract with limited term | \n\nB\n | \n−0.82% | \n0.00 | \n−2.80% | \n0.00 | \n−0.86% | \n0.08 | \n−0.30% | \n0.39 | \n2.50% | \n
_cons | \n\n | \n | 0.00% | \n0.00 | \n0.00% | \n0.00 | \n0.00% | \n0.00 | \n0.00% | \n0.00 | \n– | \n
Estimated results of room equipment (RE), building equipment (BE), and contract conditions (CC).
Marginal price effect on RE, BE, and CC.
Looking at the estimated results, as floor area increases, rent goes up, and as the number of minutes on foot from the station increases or the railway travel time from Tokyo station increases, the rent goes down. When taking a wooden structure as the baseline of the building structure, the rent will increase in the order of steel frame, RC, and SRC. The rent varies greatly depending on the ward in which the property is located; a high-rise condominium is a positive driver, and a 1F apartment positions a negative driver for rent. These results are consistent with previous studies and the intuition of market participants.
\nThe effect of the number of years since construction differs depending on the period of construction, and as a whole, there is a −0.53% reduction in rent per year after construction. However, looking at the old/main/new period of construction dummy, the speed of reduction is high for new stock and low for old stock. This shows that the effect of years since construction is nonlinear, indicating that the decline in rent will be considerably smaller after a certain number of years. Such nonlinearity is also consistent with a series of previous studies.
\nThe influence of the ancillary equipment situation on the rent changes according to the period of construction (\nFigure 1\n). The change can be classified into the following four patterns.14\n
Pattern A: Items considered to have lost value because of commonness
In Pattern A, it is assumed that the equipment premium that was once a differentiating factor for price was lost because of the advancing commonness of equipment. This corresponds to room equipment (RE) such as flooring, separate bath and toilet, balcony, toilet with washlet, and bathroom dryer and building equipment (BE) such as TV intercom, automatic entrance door, delivery locker, and so on. In all cases, the ancillary rate has increased, so the superiority of the ancillary equipment falls, the influence on rent differs between old and new stocks, and such influence is generally small in new stock. Flooring and TV intercoms have a negative impact on new stock. This indicates that flooring and TV intercoms are no longer special equipment and do not offer price advantages.
Pattern B: Items considered to have lost value because they satisfied limited needs
In Pattern B, it is assumed that the price premium of the equipment was lost because the needs the equipment satisfied were limited in the first place and have been satisfied. The walk-in closet corresponds to this in room equipment (RE), nothing corresponds to this in building equipment (BE), and free Internet and contract with limited term correspond to this in contract conditions (CC). Contract with limited term has a negative impact on rent in new stock.
Pattern C: Items for which demand is considered to be increasing but the ancillary rate is low, and value is increasing
Pattern C is such that although consumer demand is increasing over time, a price premium exists because of the low ancillary rate in the housing stock. Equipment such as a reheating bath and underfloor heating corresponds to this in room equipment (RE), and security cameras, garbage disposal available 24-hours a day, and bike parking correspond to this in building equipment (BE). Items such as use as an office correspond to this in contract conditions (CC). In particular, the reheating bath and use as an office have a significant influence of +3.45 and +3.77%, respectively.
Pattern D: Items considered to be due to other individual factors
Items for which a price premium exists due to other factors correspond to owner-owned condominium for lease in room equipment (RE) and guarantor unnecessary in contract conditions (CC). Regarding condominium for lease, the effect of the increase in supply is considered to be caused by the change in the social situation, where the tendency for relatives to avoid guaranteeing rent obligations has strengthened.
\n\nFigure 2\n shows the depreciation rate of all rents (All) and for the case where the ancillary equipment situation is poor (Poor). The equipment being poor indicates there is no (i) washlet, (ii) bathroom dryer, (iii) reheating bath, (iv) TV intercom, (iv) automatic entrance door, (iv) delivery locker, or (vii) security camera. These types of equipment have become more common in recent years and can be installed in existing buildings.
\nDepreciation in rental housing.
There were 13,033 properties with poor equipment; a regression analysis similar to the previous one was carried out with the logarithm of the rent as a target variable, and the regression coefficient of the years since construction was obtained. That is, as of October 2018, data points without the aforementioned equipment exist regardless of whether they are new, main, or old stock. This means that low-quality rental housing that does not have equipment that has become popular in recent years is still supplied. By extracting such data and comparing the depreciation of rental housing with new functions that benefited from technological progress and the depreciation of low-quality rental housing with no new functions, it is possible to extract the depreciation that accompanies obsolescence.
\nIn \nFigure 2\n, the depreciation rate for each period is calculated with the rent at the time of construction as 100 to demonstrate the theoretical effect of the increasing number of years since construction on rent. When comparing the depreciation rate of all rents with that of rents of properties with a poor ancillary equipment situation, the depreciation rate increases in all cases (new, main, and old stocks). Roughly 60 years after construction, the difference was found to be 5.5%.
\nIn addition to the measurement of the magnitude of the age effect accompanying obsolescence, this result means that rent depreciation can be mitigated if appropriate ancillary equipment investment is made with respect to the demands for housing equipment that have increased with economic growth and changes in lifestyle. We believe that this will provide pointers for high-level policy with respect to Japan’s rental housing market, where the aging of stock will advance in the future.
\nChanges in prices over time are broken down into changes due to supply-demand relationships and those caused by quality changes. In particular, this means that in markets with rapid technological progress, the price rise accompanying quality change increases as new products are introduced successively, but at the same time, in markets where such new products are introduced, the speed of obsolescence is fast.
\nCompared with Western countries, new products are easy to create in the Japanese housing market. The background to this is there are many housing providers and a comparatively large number of companies that do business throughout Japan and overseas. Such companies possess, for example, think tanks to develop new products, and are developing integrated business from large-scale procurement of raw materials to design, construction, sales, and management.
\nIn this study, we focused on the period in which the housing was supplied and clarified the types of functions and equipment supplied to the market in each period and the extent of the marginal price effect in 2018. In addition, we measured the magnitude of obsolescence that accompanies the addition of a new function.
\nThe conclusion can be summarized as follows.
Rent is strongly influenced by the floor area, years since construction, building structure, number of minutes on foot from the nearest station, railway travel time from Tokyo station, location, and so on. This confirms conclusions provided by previous studies.
The ancillary conditions of housing equipment vary greatly depending on the construction year. This suggests that the Japanese rental housing market is strongly influenced by regulations such as the Building Standards Act and the improvement of living standards by economic growth.
Some ancillary conditions have a large influence on rent, but if the ancillary rate increases, the influence becomes smaller due to commonness, and housing equipment responding to new needs have a positive influence on rent.
Even if the number of years since construction is large, depreciation of the rent can be reduced if additional investment in appropriate housing equipment is carried out.
These evaluations are for the present time, and they are expected to change in the future as housing equipment ancillary rates change and social conditions, lifestyles, and resident demands evolve. The conclusion of this study shows the possibility of increasing profitability by responding to resident demands and raising rent through adding ancillary equipment, even in countries in Europe and in the United States, where housing building restrictions are strict.
\nHowever, several tasks remain. First, it is possible to add new functionality even to housing classified as old stock through large-scale renovation investment. In this sense, this study has not been able to measure the effect of investment in renovation. Moreover, in order to generalize the study result, it is necessary to identify appropriate housing equipment according to changes in lifestyle and social conditions, in addition to the influence of housing equipment on rent. Even if the scope is restricted to Japan, it is also necessary to consider points such as the type of differences that arise depending on the scale of the city and the standard of living and climate in different regions, as well as whether the necessary housing equipment differs according to the age, gender, family composition, income, and so on of the residents.
\nWe would like to clarify these questions as future research tasks.
\nThe second author gratefully acknowledges the financial support of the Nomura Foundation.
\nIntechOpen's Authorship Policy is based on ICMJE criteria for authorship. An Author, one must:
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