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The energy efficiency of existing buildings is a crucial factor in addressing energy consumption challenges in European countries, accounting for nearly 40% of the total energy usage. One such country is Cyprus, which faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. To face this situation, extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings erected before 2010, which lack any energy efficiency measures. This study examines the case of the Pano Polemidia Cultural Hall (PPCH), which represents the building stock of that period. Through the simulation of two scenarios, before and after the adoption of retrofit measures, the existing energy performance is initially evaluated and then the adoption of sustainable solutions, which improve substantially the energy efficiency and can be easily adopted from the relevant authorities, is explored. These retrofit measures include installation of HVAC system, covering of the shell of the building with external thermal insulation, lighting replacement with LED devices, installation of PV system and solar panels, and replacement of the external openings with aluminum windows. The results derived show that the energy consumption of the building was reduced from 468 to 218 kWh/m2·yr, with renewable energy sources (RESs) contributing 177 kWh/m2·yr, the CO2 emissions were reduced from 136.73 to 11.5 kg/m2·yr, while the reduction in energy consumption per sector ranged from 25% in lighting to 83% in hot water. Therefore, it is evident that a comprehensive retrofitting plan can transform the PPCH into a near-zero energy consumption building that also provides value to the local community and can act as a successful example for any other non-residential buildings with similar characteristics.

In recent years, the desire and requirement for green buildings have increased. The aim of this research is to determine and confirm the increased request for green properties and to investigate whether this is related to a new need or simply a desire of buyers. Moreover, the paper examines people’s knowledge of greenness and sustainability and their wish to live and work in sustainable buildings. The methodology used for this research is based on quantitative research methods with the use of questionnaires to better understand the residents’ awareness, needs, and desires related to sustainability. The research was based on the hypothesis that increased knowledge and awareness of sustainable design can affect the real estate market. Secondly, this research examined whether the increased desire and need for sustainable buildings may increase the market value of sustainable buildings and if people with higher incomes desire green buildings more. Finally, the last hypothesis examined regarded the differences between residential and commercial buildings in terms of sustainable design. The study explored whether buyers will pay extra to purchase a sustainable property and how sustainability can affect the market value and the construction industry. The participants who took part in the research study were living and working in Cyprus. One of the significant outcomes was the fact that people who have knowledge and awareness related to sustainability are willing to pay extra to purchase green properties. Another interesting outcome was that most people have knowledge of sustainable building design. This awareness is crucial as people’s desire is the strongest driver, which can influence them to invest more in green real estate.

This study employs the statistical method of Multiple Linear Regression analysis (MLR) to develop an Automated Valuation Model (AVM) for estimating land values by utilizing transaction-based data in Limassol, Cyprus. The authors focus on the confidence level and accuracy of the value estimated by an AVM. Thus, the developed AVM was tested in two contrasting areas of Limassol in terms of location characteristics and market conditions. Most AVMs contain a statistical method to generate the estimated value of a real estate property. However, the outcome of a statistical method is verified by statistical measures. Therefore, if the validation of the predicted value for its accuracy derives from the statistical metrics of the model, then the explanatory variables cannot remain constant. It is implied that the AVM in order to grant the highest statistical metrics for a given property valuation requires different combination of independent variables in different locations, which means that the parameters of the model should change or adjust for every case to obtain the best fit model. The authors demonstrate that the best fit model is obtained when several models are executed with alternative combinations of variables. Hence, the best fit to the regression is given by the model with the better statistical measures when compared to the other models. Consequently, the predicted value is supported by statistical significance and can be adopted at a high confidence level.

Property valuation evolved from simple empirical judgements to automated valuation models and their application have extended from single property to mass valuation. Many governments across the world have used AVMs to get a valuation in thousands of properties for tax related purposes. The literature review is extensive and it is growing day by day. The island of Cyprus was introduced to computer assistant mass appraisal (CAMA) in 2013 when the Department of Land and Surveys (DLS) performed a general valuation and then to revaluation in 2018.  The aim of this research is to provide more transparency to the reliability of the data used in the latest general valuation. An automated valuation model was developed, using the MRA method and Hedonic Pricing Model, to test the performance of the data and compare them with the minimum standards a valuation model should have according to the International Association of Assessing Officers (IAAO).  A case study using a holdout sample with data from Lakatamia Municipality was created to observe the reliability of the data but also to improve the accuracy of the Automatic Valuation Model. Three regressions were carried out: a) Basic regression with 503 observations and 10 variables, b) Regression with the previous variables plus 10 nearest neighbors as predictors and c) Regression with the previous variables plus 10 nearest neighbors as predictors, with 450 best observations – deleted outliers based on absolute error. The coefficient of determination (R-squared) measures the goodness of fit of the regression line, in other words, how close the data are to the estimated line. Initially the R-squared was 0.319 which is above IAAO standards but it was increased to 0.765 after the application of the third model. This accuracy showing better performance than the mass valuation system applied by the Department of Land and Surveys in Cyprus with accuracy of 0.384 Concluding the research ends with a critical discussion about the reliability of the data and some suggestions that could be applied by the DLS to improve the performance of the data.  It is worth mentioning that the Cypriot data have a limitation due to the high heterogeneity found between properties.

This research suggests improvements to the macroeconomic housing
indices of a thin real estate market, such as that of Cyprus, by testing
various index construction methods with transaction-based data.
Authors employ around 80% of the total number of apartment transfers
documented at the Department of Lands and Surveys (DLS) of
Cyprus, spanning from the first quarter of 2015 to the second quarter
of 2022. They utilize this data to generate comprehensive indices
at both the national and district levels. Authors studied, analyzed,
and identified the deficiencies of the DLS database and tested the
sample with six different methods. Log-linear time dummy hedonic
models were found to explain the variation of prices better than
other methods, mainly due to their ability to handle the diversity of
properties in terms of location and physical characteristics and proposed
techniques to deal with the issues of the standard time
dummy (STD) and rolling time dummy (RTD) methods, regarding
index revisions and low transaction volume during periods of downturns,
respectively. Furthermore, a hybrid dependent variable of
actual and appraised prices, that is, the accepted price, extracts
explicit significantly better statistical measures. Additionally, the overall
model fit was enhanced by introducing locality dummy variables
and, through different combinations of attributes, captured the optimal
results per district. Eventually, when the introduced transaction based
indices were compared to the corresponding existing published
indices, which are based on non-actual data, we saw some
resemblances, but overall, there were wide deviations.

A lot of discussion takes place lately about whether an AVM can replace the valuations made by humans. But what is an AVM? AVM stands for "Automated Valuation Model." It is a mathematical model – computer based- that uses statistical techniques, algorithms, and data to estimate the value of a property.