scholarly journals Farmland value in the “Conegliano Valdobbiadene Prosecco Superiore PGDO” area. An application of the Hedonic Pricing method.

Aestimum ◽  
2021 ◽  
Vol 78 ◽  
pp. 5-33
Author(s):  
Tiziano Tempesta ◽  
Isabella Foscolo ◽  
Nicola Nardin ◽  
Giorgio Trentin
Keyword(s):  
Statistical Significance ◽  
Hedonic Pricing ◽  
Coefficient Of Determination ◽  
Land Market ◽  
Model Parameters ◽  
Absolute Deviation ◽  
Market Prices ◽  
Land Prices ◽  
Hedonic Pricing Method ◽  
Territory Characteristics

In the last 30 years, numerous studies analysed the factors that affect land prices mainly using the Hedonic Pricing method. These studies have shown that many factors can affect land prices (e.g. land and surrounding territory characteristics, accessibility, proximity to urban area, etc.). However, they rarely addressed the analysis of the reliability of the models by comparing the estimated values to the observed one. Attempting to face this problem, our study analysed the land market of the “Conegliano Valdobbiadene Prosecco Superiore PGDO” area. Despite the quite high coefficient of determination (r2 = 0.76) and statistical significance of the model parameters, we found that the percentage absolute deviation between observed and estimated value is higher than 30% in 34% of cases. Our results seem to suggest that future researches should devote particular attention to the analysis of the discrepancies existing between estimated values and market prices in order to support the appraisal activity of professional valuers.

scholarly journals Official agricultural land price in the Slovak Republic

2008 ◽  
Vol 53 (No. 4) ◽  
pp. 184-188
Author(s):  
K. Bradáčová
Keyword(s):  
Property Rights ◽  
Agricultural Land ◽  
Slovak Republic ◽  
Supply And Demand ◽  
Income Effect ◽  
Land Market ◽  
Land Price ◽  
Market Prices ◽  
Land Prices ◽  
Land Site

As long as the land market in Slovakia is not completely developed and land market prices introduced, the officially assigned land prices are practically in use. At the present time, land prices should express the supply prices, which cover the income effect of the land site under the socially necessary costs. In this situation, for the temporary period, centrally assigned fixed land prices could represent the effective supply and demand prices in case they correspond to the mentioned conditions. At present, the official prices are used for fiscal purposes and the land property rights.

scholarly journals Land market development in the Czech Republic

2012 ◽  
Vol 51 (No. 5) ◽  
pp. 216-220 ◽  
Author(s):  
E. Vrbová ◽  
J. Němec
Keyword(s):  
Czech Republic ◽  
Agricultural Economics ◽  
Market Development ◽  
Land Market ◽  
The West ◽  
Market Prices ◽  
The Czech Republic ◽  
Land Prices ◽  
Eu Countries ◽  
Agricultural Use

Land market in the Czech Republic is monitored by the Research Institute of Agricultural Economics on the sample of 24 districts (1/3 of the CR). Land prices depend on the area, culture and region of the plot. Sales of small plots (up to 1 ha) prevail. These plots are usually purchased for non-agricultural use and their prices are many times higher than prices of large plots (above 5 ha) which are usually bought for agricultural purpose. Land market is not well developed, only 0.2–0.4% of the monitored area is sold each year. But in the last years, it is increasing. Compared with land prices in the west EU countries, land market prices in the CR are low.  

Äquivalente Steuerfinanzierung kommunaler Leistungen

2004 ◽  
Vol 53 (1) ◽  
Author(s):  
Astrid Lemmer
Keyword(s):  
Public Goods ◽  
Decision Makers ◽  
Private Good ◽  
Land Market ◽  
Land Values ◽  
Local Public Goods ◽  
Market Prices ◽  
Land Prices ◽  
Benefit Principle ◽  
Market Needs

AbstractIn this paper a local tax to finance local public goods is discussed. The intention is to tax citizens upon their willingness to pay for public goods. The idea to base the tax on a private good that indicates the value of public goods is considered. Land is a private good that satisfies the condition of being immobile in a community. First, the land market needs to be looked at to ensure that land prices are market prices. Once this is demonstrated it is examined how a tax based on land prices can be considered to be a tax that fulfils the benefit principle. Although there are various other influences on the price of land, it is shown that a tax on land values - or to be more precise: on land prices - is not only a neutral tax, but gives incentives for political decision makers to efficiently provide local public goods and assures that those who receive the benefits pay for it.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

scholarly journals Spatio-temporal modeling of surface runoff in ungauged sub-catchments of Subarnarekha river basin using SWAT

MAUSAM ◽  
2021 ◽  
Vol 72 (3) ◽  
pp. 597-606
Author(s):  
CHINMAYA PANDA ◽  
DWARIKA MOHAN DAS ◽  
B. C. SAHOO ◽  
B. PANIGRAHI ◽  
K. K. SINGH
Keyword(s):  
Surface Runoff ◽  
Assessment Tool ◽  
Nutrient Loss ◽  
Coefficient Of Determination ◽  
Annual Rainfall ◽  
Model Parameters ◽  
Runoff Generation ◽  
Temporal Modeling ◽  
Spatio Temporal ◽  
R Factors

In this present study, Soil and Water Assessment Tool (SWAT) embedded with ArcGIS interface has been used to simulate the surface runoff from the un-gauged sub-catchments in the upper catchment of Subarnarekha basin. Model calibration and validation were performed with the help of Sequential Uncertainty Fitting (SUFI-2) in-built in the SWAT-CUP package (SWAT Calibration Uncertainty Programs). The model was calibrated for a period from 1996 to 2008 with 3 years warm up period (1996-1998) and validated for a period of 5 years from 2009 to 2013. The model evaluation was performed by Nash - Sutcliffe coefficient (NSE), Coefficient of determination (R2) and Percentage Bias (PBIAS). The degree of uncertainty was evaluated by P and R factors. Basing upon the R2, NSE and PBIAS values respectively, of the order of 0.90, 0.90 and -12%, during calibration and 0.85, 0.83 and -15% during validation, substantiate performance of the model. All uncertainties of model parameters have been well taken by the P and R factors respectively, of the order of 0.95 and 0.77 during calibration and 0.82 and 0.87 during validation. The runoff generation from 19 sub-catchments of Adityapur catchment varies from 29.2-44.1% of the annual rainfall and average surface runoff simulated for the entire catchment is 545 mm. As the surface runoff generated in most of the sub-catchments amounts to above 30% of rainfall, it is recommended for adequate number of structural interventions at appropriate locations in the catchment to store the rainfall excess for providing irrigation, recharging groundwater and restricting the sediment and nutrient loss.

scholarly journals CONCEPT OF ECONOMICAL‐MATHEMATICAL MODEL OF LAND MARKET

2007 ◽  
Vol 13 (4) ◽  
pp. 333-340
Author(s):  
Gintautas Šatkauskas
Keyword(s):  
Mathematical Model ◽  
Input Parameter ◽  
Market Price ◽  
Research Process ◽  
Land Market ◽  
Market Prices ◽  
Noise Background ◽  
Input Parameters ◽  
Main Components ◽  
Sufficient Precision

Input parameters, ie factors defining the market price of agricultural‐purpose land, are interrelated very often by means of non‐linear ties. Strength of these ties is rather different and this limits usefulness of information in the research process of land market prices. Influence of input parameter changes to the input parameters in case when there are rather substantial changes may be determined in someone direction with a sufficient precision, whereas in other directions with comparatively small changes of input parameters this influence is difficult to be separated from the “noise” background. Taking into account the above‐listed circumstances, the concept of economical‐mathematical model of land market should be as follows: there is carried out re‐parameterisation of the process by means of introduction of new parameters in such a way that the new parameters are not interrelated, and the full process is evaluated at the minimal number of these parameters. These requirements are met by the main components of the input parameters. Then normalisation of the main components is carried out and dependencies on new parameters are determined. It is easier to interpret the dependencies obtained having reduced the number of input parameters and the higher the non‐linearity of interrelations of primary land market data, the greater effect of normalisation of input-parameter components. The results are compared with the valuations of experts.

A study about the linearity of trends between extreme and mean temperatures in Argentina

2021 ◽  
Author(s):  
Solange Suli ◽  
Matilde Rusticucci ◽  
Soledad Collazo
Keyword(s):  
Linear Regression ◽  
Linear Relationship ◽  
Statistical Significance ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Mean Temperature ◽  
Change In The Mean ◽  
The Mean ◽  
Student’S T ◽  
Cold Extremes

<p>Small variations in the mean state of the atmosphere can cause large changes in the frequency of extreme events. In order to deepen and extend previous results in time, in this work we analyzed the linear relationship between extreme and mean temperature (Τ) on a climate change scale in Argentina. Two monthly extreme indices, cold nights (TN10) and warm days (TX90), were calculated based on the quality-controlled daily minimum and maximum temperature data provided by the Argentine National Meteorological Service from 58 conventional weather stations located over Argentina in the 1977–2017 period. Subsequently, we evaluated the relationship between the linear trends of extremes and mean temperature on a seasonal basis (JFM, AMJ, JAS, and OND). Student's T-test was performed to analyze their statistical significance at 5%. Firstly, positive (negative) and significant linear regressions were found between TX90 (TN10) trends and mean temperature trends for the four studied seasons. Therefore, an increase in the Τ-trend maintains a linear relationship with significant increase (decrease) of warm days (cold nights). Moreover, we found that JFM was the one with the highest coefficient of determination (0.602 for hot extremes and 0.511 for cold extremes), implying that 60.2% (51.1%) of the TX90 (TN10) trend could be explained as a function of the Τ-trend by a linear regression. In addition, in the JFM (OND) quarter, the TX90 index increased by 7.02 (6.02) % of days each with a 1 ºC increase in the mean temperature. Likewise, the TN10 index decreased by 4.94 (and 4.99) % of days from a 1ºC increase in the mean temperature for the JFM (AMJ) quarter. Finally, it is worthwhile to highlight the uneven behavior between hot and cold extremes and the mean temperature. Specifically, it was observed that the slopes of the linear regression calculated for the TX90 index and Τ presented a higher absolute value than those registered for the TN10 index and Τ. Therefore, a change in the mean temperature affects hot extremes to a greater extent than cold ones in Argentina.</p>

Abstract WP291: Stochastic Methods Can Resolve the Dilemma of Emergency Stroke Transport

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Daniel A Paydarfar ◽  
David Paydarfar ◽  
Peter J Mucha ◽  
Joshua Chang
Keyword(s):  
Census Data ◽  
Infarct Volume ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Practical Significance ◽  
Stochastic Methods ◽  
Model Parameters ◽  
Collateral Flow ◽  
Acute Stroke Care

Introduction: Drip and Ship (DNS) and Mothership (MS) are well-known emergency transport strategies in acute stroke care, but the criteria for choosing between the two is widely debated. Existing models define time-dependent outcomes but cannot resolve this debate with statistical significance because the independent variables are deterministic. We propose a novel stochastic framework that quantifies statistical significance between DNS and MS in a network of primary and comprehensive stroke centers. Methods: We represented the physiology of ischemic core growth as a stochastic first-order differential equation, enabling infarct volume at time of reperfusion to be calculated and mapped to 90-day mRS. Using Texas as a case study, we configured the state’s stroke network within 15,811 geographic blocks as defined by census data. For each block, we ran Monte Carlo simulations to generate Beta distributions of large- and small-vessel infarct volumes, which were then translated into cumulative distribution functions of mRS. A two-sample Kolmogorov-Smirnov test for significance, and Cohen’s d effect size statistic for practical significance were computed between each DNS and MS pair. Stable effect sizes were assured by sampling > 5,000 total infarct volumes for each block. All model parameters were established from large cohort studies or trials. Results: Of the 13,113 blocks where the primary stroke center is the closest hospital from origin, DNS produces significantly better stroke outcomes than MS in 79.0% (0.3% SEM; P < 0.05; 0.2 < d < 0.5). For the subset of patients with large-vessel strokes, MS produces significantly better outcomes in 44.6% of blocks (1.3% SEM; P < 0.05; 0.4 < d < 0.85). Conclusion: Stochastic methods enable the use of clinically relevant metrics for comparative significance of DNS and MS in a geographic region. This formalism, which has not been incorporated in previous models, can be further generalized beyond stochastic infarct volumes if sufficiently large datasets become available. For example, the kinetic growth model can integrate the statistical distributions of times (pre-hospital and hospital) leading up to intervention, and patient attributes that affect outcomes, such as the degree of collateral flow and comorbidities.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2012 ◽  
pp. 308-320
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Hedonic Pricing ◽  
Spatial Lag Model ◽  
Hedonic Pricing Method ◽  
Spatial Weights Matrix ◽  
Spatial Regression Models ◽  
Gis Environment ◽  
Value Of Irrigation Water

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

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