隨著社會經濟結構的轉型，目前台灣已邁向服務業為主的社會型態。為應該服務業及商業所需的辦公空間，因此辦公大樓陸續的興建。隨著近年來不動產的投資興盛，辦公大樓成為了外資或者保值的投資選擇。而判斷辦公大樓價值的標準即為該大樓所能創造的收益為何。辦公大樓的收益為租金，因此了解影響租金組成的因素，能夠提供我們在進行投資決策時一個參考的依據。但過去已有許多租金影響因素之研究，是以本研究擬從不同的觀點切入來探討租金的成因。 近十年來，國內許多豪宅或者知名的辦公大樓引進入物業管理的方式，取代了過去由管理委員會主導的管理模式。物業管理的興起，乃是人們對於住宅安全以及管理品質引申需求。對於物業管理的研究，國內目前仍著重在管理績效的衡量以及利用電腦提升物業管理效率的部分，鮮有探討辦公大樓租金與物業管理之間的關連。是以，本研究以台北市六行政區的辦公大樓為標的，利用特徵價格模型(Hedonic Price Model)和BOX-COX轉換，從物業管理的觀點來探討辦公大樓租金的影響因素。 / In Taiwan, business has become the main economic activity. The more prosperous business is, the more office spaces are requested. To understand the composition of office rent, hedonic price model is used as an analysis tool frequently. The varieties, such as age, distance of MRT, or standard floor, are usually used as the hedonic varieties. When property management is getting popular in the decade, there are few research use property management as varieties. To understand if the difference of property management style would affect the office rent, this essay used Box-Cox translation and Semi-Log regression to analyze the relationship between property management and office rent.

This study assess Prague real estate market from price level bubble existence point of view. For this purpose construct loglinear regression models for estimating price level of purchase and renting for several segments on the real estate market. These estimated price levels afterwards compare to theoretical price levels set by user cost concept, which employs fundamental factors such as capital costs, depreciation rates, personal income and property taxes, additional asset risk and expected capital gain. Study concludes that Prague real estate market does not currently experience positive price bubble, even one of the segments assess as underpriced.

Over the last decades, economics as a field has experienced a profound transformation from theoretical work toward an emphasis on empirical research (Hamermesh, 2013). One common constraint of empirical studies is the access to data, the quality of the data and the time span it covers. In general, applied studies rely on surveys, administrative or private sector data. These data are limited and rarely have universal or near universal population coverage. The growth of the internet has made available a vast amount of digital information. These big digital data are generated through social networks, sensors, and online platforms. These data account for an increasing part of the economic activity yet for economists, the availability of these big data also raises many new challenges related to the techniques needed to collect, manage, and derive knowledge from them. The data are in general unstructured, complex, voluminous and the traditional software used for economic research are not always effective in dealing with these types of data. Machine learning is a branch of computer science that uses statistics to deal with big data. The objective of this dissertation is to reconcile machine learning and economics. It uses threes case studies to demonstrate how data freely available online can be harvested and used in economics. The dissertation uses web scraping to collect large volume of unstructured data online. It uses machine learning methods to derive information from the unstructured data and show how this information can be used to answer economic questions or address econometric issues. The first essay shows how machine learning can be used to derive sentiments from reviews and using the sentiments as a measure for quality it examines an old economic theory: Price competition in oligopolistic markets. The essay confirms the economic theory that agents compete for price. It also confirms that the quality measure derived from sentiment analysis of the reviews is a valid proxy for quality and influences price. The second essay uses a random forest algorithm to show that reviews can be harnessed to predict consumers’ preferences. The third essay shows how properties description can be used to address an old but still actual problem in hedonic pricing models: the Omitted Variable Bias. Using the Least Absolute Shrinkage and Selection Operator (LASSO) it shows that pricing errors in hedonic models can be reduced by including the description of the properties in the models.

不動產法拍屋市場中，是否受到總體經濟環境變化或金融風暴來襲，而影響拍定價相對於第一拍底價之比率？是本研究所探討的課題。除上述總體經濟因素外，是否尚有其他因素如法拍屋個別屬性影響拍定價對底價比？本文藉由上述各項因素存在與否，探討法拍屋拍定價對底價比和各變數間之關係，得以建立法拍屋拍定價對底價比之變數模型。 本研究以特徵價格理論探討影響台北市士林區法拍屋拍定價對底價比之變數，並以迴歸分析方法分析影響法拍屋拍定價對底價比之因素，及其影響程度，並藉此探討當市場發生重大金融事件或總體經濟環境變化時，是否影響法拍屋之拍定價對底價比。實證結果發現：影響法拍屋拍定價對底價比之顯著變數如下： 一、法拍屋個別屬性：是否為一樓、競標數呈正相關，屋齡呈先負後正曲線關係，拍定次數呈負相關。 二、總體經濟變數：拍定前月放款利率、拍定前季是否經濟衰退或金融風暴呈負相關。 本文探討影響法拍屋拍定價對底價比之變數，研究結果期能提供地方法院訂定第一拍底價及酌減各拍次底價之參考、投標人決定投標價及選擇最佳進場時機之參考、地方法院委託之不動產鑑定人於法拍屋估價時，價格調整幅度權衡的參考、以及債權人決定是否執行及續拍之參考。 / Real estate markets are affected by macro economic factors, regional factors and individual factors. As there is recession in economy growth, the number of foreclosed properties increased. How are the prices of foreclosed properties influenced by the aforementioned factors? This study intends to explore factors affecting the ratio of the final transaction price to the listed base price in the real estate auction markets through the Hedonic Price Model. Results show that the first-floor effect, number of bidders and age of the buildings are positively related to the ratio of the final transaction price to the listed base price in the real estate auction markets. Numbers of auctions, interest rates, and the occurrence of the financial crisis in the previous season are negatively related to the ratio of the final transaction price to the listed base price. Results of this study provide precious reference value on formulating the listed base prices on the real estate auction markets. Investors can also take into account of the significant factors in making the investment decision in the auction market.

過去都市更新相關文獻中，多在分析都市更新制度與探討相關法令缺失，且多以權利變換方式探討地主權益之分配，較少以量化分析來研究都市更新協議合建分配型態，對於地主所關注合建分配比率之影響因素也未能深入研究。本研究透過文獻回顧與台北市都市更新之現況分析後，掌握可能之影響變數，藉由實證資料之蒐集與相關變數之選取，將資料進行複迴歸實證分析，以探討影響都市更新合建分配比率之顯著變數。 本研究篩選出可能影響合建分配比率之14個自變項，並排除相關係較高之土地所有權人數及建築成本兩變數後，透過複迴歸分析，結果顯示有8項達顯著性之影響因素，其中個別屬性變數包括每人土地面積、每容積公告土地現值、平均容積率、臨路寬度與臨路寬度平方、都市更新容積獎勵率、區域房價等6項達顯著性，除臨路寬度平方與都市更新合建分配比率呈負向關係外，其他皆呈正向關係，而在總體經濟變數有上一月基準利率及上一季M2年增率等2項達顯著性，其中上一月基準利率與都市更新合建分配比率呈負向關係，而上一季M2年增率與都市更新合建分配比率呈正向關係。 由實證結果得知，增加都市更新容積獎勵，確實有助提昇地主合建分配比率，因此，政府應適度調整容積獎勵項目及上限，使地主參加都市更新後，能獲得較佳之居住環境與較高之居住面積水準，甚至保障地主原居住面積水準，以增加地主參與都市更新之意願。期望政府持續大力推動都市更新政策，透過建築規劃及設計，達到改善市容、美化環境、增進公共安全及提昇居住水準等政策目的，進而達到政府、地主及開發商三贏之目標。 / Most of the past research studies on urban renewal involves analysis in the related regulations and their shortcomings. Furthermore, studies based on allocation of land owners’ equity rights are mostly done from perspective of the rules set from the government supervised appraisal process known as Rights Transformation. Few of them are conducted using the quantitative approach to derive the equity rights of the landowners from the perspective of the process known as Joint Development --- a process of private negotiation and derived terms. Thus, through analysing research articles and the current state of the urban renewal projects in Taipei, this study first intends to identify the possible variables that might affect the equity share distribution. Then using regression analysis based on the selected variables and collections of empirical data, this study explores and derives variables of significant impacts on the equity share distribution between developers and landowners in jointly developed urban renewal projects. This research has screened 14 independent variables that might have impact the equity share distribution. Results of regression analysis identify 8 significant factors, including land area per owner, government assesed land value per floor area, average floor area ratio, road width, square of road width, floor area incentive for urban renewal, and surrounding area housing value. Most of the variables have positive relationships with the landowners’ equity except for the square of road width. Macroeconomic variables such as last month’s prime interest rate and M2 growth rate in the previous quarter are found to have significant impact as well. Of which, prime interest rate is found to have negative relationship with landowners’ equity, and M2 growth rate is found to have positive relationship. Based on this empirical study, the result shows that increasing urban renewal floor area incentives indeed enhances landowners’ equity rights in a jointly developed urban renewal project. Thus, government should adjust appropriately each of the floor area incentives and their maximum allowed limits, giving the original residents a better living environment and a slightly larger living space from the redeveloped project. Furthermore, to increase the willingness of the original residents’ participation in urban renewal projects, the government could guarantee the retention of their floor area in the redeveloped projects to be the same as the floor area before urban renewal. The government should continue to promote its urban renewal policy. Proper architecture planning and design will improve the city’s appearance and the environment, as well as enhancing public safety and living standards - through which the government will not only achieve its policy objectives, but also create a win-win situation between the developers, landowners, and the government itself.

To increase real estate values, developers often apply designs on the land. In the case of a single family housing development, the designs are applied to the unit of subdivisions. In this study, the designs are defined as “value creation concepts,” which increase housing values at the subdivision level. The value creation concepts are classified into five categories – the sense of arrival, product mix, walkability, circulation system, and amenity. This cross-sectional study focuses on exploring the effects of value creation concepts in the subdivision. Two methodologies – the Hedonic Price Model (HPM) and the Hierarchical Linear Model (HLM) – are used to test whether or not the value creation concepts would increase or decrease single family housing values. The study sample is composed of 6,562 single family houses nested in 85 subdivisions in College Station, Texas. Data are composed of two levels: the housing level and the subdivision level. The scores of the sense of arrival were provided by sixtyone graduate students at Texas A&M University using photograph evaluations. Most structural variables were obtained from the Brazos County Appraisal District, and physical environmental variables were objectively measured using the Geographical Information System. In the both models, sense of arrival, greenway connectivity, sidewalk connectivity, and median length of cul-de-sac variables have positive effects on single family housing values while phased project, the number of accessible entrances, street density, single family density, and median length of block variables have negative effects on single family housing values. At the housing level, several structural variables (e.g. bathrooms, attached garage, porches, etc), attached to a golf course, sports facilities, network distance from the nearest elementary school, population density, and personal variables (i.e., tenure, workable age, employment) were significant (p<.05) predictors of single family housing value. Findings support that the value creation concepts have effects on increasing housing values at the subdivision level, which would provide thoughtful insights for developers in residential areas. In addition, the HLM can be used as the complement of the HPM by controlling interaction terms between housing variables and subdivision variables, or among the subdivision variables themselves.

Production of electric energy is continuously affected by many factors. Therefore, tools for predicting the future production are needed. In turn, the production affects the electric energy price, which is set on electric energy exchanges. This thesis is intended to find out if the software SDDP can be used for hydrothermal power production simulations in the Nord pool area. By building a simplified model of the electric energy production in Sweden with a focus on hydro, thermal and wind power, the intention is to see how the model is affected by different conditions. The investigated conditions are several; higher and lower water inflows to the hydro power reservoirs; different amounts of installed wind power production; different price levels of emission allowances for CO2. By using the simulation software SDDP, more wind power was seen to lower the electric energy prices, as well as reduce the need of transmission of power from the northern to the southern parts of Sweden. In the simulation, Sweden was divided into four areas, connected where the main bottlenecks in the power grid are located. Water inflows to the reservoirs are crucial in the model. Actual inflow data can be bought from SMHI. However, due to the limited thesis budget, estimations were constructed instead. The estimations were difficult to make and turned out to be too high. Consequently, no reliable evaluation of the SDDP software could be done using this data.

近年來，公部門或私部門多有土地價值評估之需要，現今土地交易課稅稅基與私部門投資抵押評估上，多未考量土地與建物個別因素應相互影響價值之因素，導致已開發土地價值評估產生偏誤，故如何合理自房地價格中分離已開發土地價值，是現行土地評價之重要課題。在房地價格分離之相關研究中，如何精準區分土地和建物價值影響因素為其關鍵的基石，而不同房地價格分離方法間差異，亦為本研究探討之議題。 本研究利用特徵價格模型分析方法，以臺北市區分所有建物之住宅交易案例為研究對象，建立複迴歸模型以分離房地價格。實證結果指出臺北市房地價格隨屋齡增加，呈現先遞減後遞增之變化，驗證高屋齡下土地再開發價值之存在，並發現各屋齡分層間土地移轉面積、管理維護對房地價格影響程度不同，藉以有效區分屋齡對土地與建物價值之影響；而現行實務上常見之土地抽取法，在分離房地價格上恐有高估已開發土地價值，以及放大價值漲幅與縮小價值跌幅之問題，顯示其建物成本價值評估過於僵固，將產生偏誤，使建物價值無法有效隨市場景氣變動。此外，透過分量迴歸模型檢驗，其結果顯示已開發土地價值與高屋齡效果間呈現正向關係，亦指價值愈高，其土地再開發價值影響愈顯著。最後，本研究統整相關實證結果，釐清現行房地價格分離上之問題，盼能改善與提升土地價值之評估能力與精準度。 / In recent years, land value assessments have been needed for different purpose. The current assessing of land transaction taxable base and mortgage demands does not consider the individual factors of lands and structures should affect the value of each other, resulting in the developed land value assessment has generated errors. Therefore, how to decompose housing price reasonably and analyze the developed land value is important for land value assessments. 　　This study establishes multiple regression model by selecting residential condominium sale data in Taipei City as sample, in order to decompose the housing price into developed land and structure value. The empirical results show that the reversion of housing price is due to the redevelopment value, and land area and level of maintenance have impact on housing price in different age variables. Besides, we also find that it has a positive relationship between developed land value and redevelopment value effect on price, and land extraction method probably has some problems in overestimation of developed land value, resulting in miscounting of land price fluctuation. According to the aforementioned, this study concludes with some issues of decomposition housing price for improving and enhancing the accuracy of land value assessments.

Thesis Price models and SLA for shared services of data centers has as objective to establish best practice, so-called "best practices" of SLA and price models for data center services. Work is divided into two parts, theoretical and practical part. The theoretical section is wants to familiarize the reader with the basic concepts such as outsourcing, reasons and risks of use. I describe the base models for providing IT services as well as pricing models for services as described in the literature. I will focus on data centers and TIER standard categorization, too. The practical part is based on an analysis of information that I got from an interview with the experts working for the biggest data center companies in the Czech Republic. Based on this information, I tried to compile the best practices for creating pricing models for data center services that are actually used in practice. Specifically, the server housing, server hosting and virtual server services. Also, for these services create an SLA that can be used by some data center company in the Czech Republic.

The thesis introduces several methods of real estate price modelling suitable either for prediction of the housing prices or for exploring the relationships between the price and its determinants. We compared the conventional linear regression approach to the tree-based methods of machine learning. The comparison analysis on the dataset of 28 019 apartments in Prague suggests that regression trees (especially the Random forest) yield a higher accuracy in the price prediction. Another objective was to examine the effects of location attributes (especially its accessibility and environmental quality) on the prices of nearby apartments. To address the spatial interactions in the geographical data, we employed three spatially conscious models to achieve more reliable results. The local analysis performed with the geographically weighted regression confirmed the presence of spatial heterogeneity and described the price effects relative to the location. In some areas, an increase of 100 meters in distance from the nearest metro station and the nearest park are associated with a decrease in the apartment prices by 644 CZK/m2 and 916 CZK/m2 , respectively. These findings are especially important for the apartments near the stations of the new metro line, which is currently in construction.