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Peer-Review Record

Estimating Residential Property Values on the Basis of Clustering and Geostatistics

Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Geosciences 2019, 9(3), 143; https://doi.org/10.3390/geosciences9030143
Received: 7 February 2019 / Revised: 20 March 2019 / Accepted: 21 March 2019 / Published: 24 March 2019
(This article belongs to the Special Issue Geodesy and Geomatics Engineering)

Round  1

Reviewer 1 Report


The paper is quite interesting.

In my opinion the methodological approach is not particularly innovative but the author has used rather common tools in a coherent way and with good results.

Even the literary background is rather full-bodied and comprehensive.

I recommend publication in this form.


Author Response


The author thank to the anonymous reviewer for his work and attention given on this paper.

The text was verified by native speaker, the English lecturer.


Reviewer 2 Report


Please, see the annotations in the attached pdf file.

Comments for author File: Comments.pdf


Author Response

 

The author thank to the anonymous reviewer for their work and attention given on this paper.

I have got comments that helped in getting this article much better and will help me also in next research.

According to the editor suggestions the new parts of the article are written in red.

The text was verified by native speaker, the English lecturer.

Author Response File: Author Response.docx


Reviewer 3 Report


The Author presented research relevant to implementation automatic models to valuation real estate. Generally, scope of the research is very up-to-date and lively in wider international discussions. However, the presented specific results and approach has been already well studied field and the analysis is very basic and I cannot see any novelty. Moreover the presented findings consider by the Author as a new or innovative where overthrown to some extent 15 years ago by the researchers related to property market analyses. Therefore just a few major reviewer doubts to the specific assumptions and results were highlighted:


Ver.61. – "The multidimensional nature of geographical space is usually ignored and the impact of a property’s situation reduced to an analysis of its location and neighbourhood, treated as environmental features." 


This statement seems to be misbelief. First it’s not true that geographical space is usually ignored that even Author confirmed this fact by presenting many authors that made an implementation of it. Furthermore, the Author claimed that researchers take only into account location and neighbourhood instead of nature of geographical space during the real estate market analyses. The question is: what then the nature of geographical space is? To differentiate it from  location and neighbourhood?


Additionally the Author actually didn’t even define any location feature or neighbourhood, and try to classify real estate market taking into account just coordinates which is illogical when we want to create the real estate valuation chart (consider valuation map). It seems that Author didn’t consider fundamental knowledge about the real estate market which is specific domain of the analyses in which we can observe: uncertain, imprecision of property information, the sudden and unpredictable changes, absence of homogenous functional dependencies between real estate attributes, significant differences between real estates etc.


Ver.77 – "The most frequently used cartographic method are isolines or choropleth maps." and ver. 83.


What is the basis of such statement?


Ver. 88 – This is not new approach, for sure. eg: Maclennan, D & Tu, Y 1996, 'Economic perspectives on the structure of local housing systems' Housing Studies, vol. 11, no. 3, pp. 387-406. and many others.


Ver.215 – in the paper should be insert: the formula to update the transaction and verification of the model. Moreover, the Author didn’t present the specific information about the attributes and their domains. This has direct influence on the selected method results and their quality etc.


Ver. 221 – one of the attributes is the "standard of the flat", as a matter of fact that kind of attribute does not exist in the national property registry, so the question is: How the Author obtained this attribute for 1873 transactions from 2007 -2011 years?


Ver. 218 – the verification of the significance the correlation results should be presented.


Ver. 228 – the method of k-means clustering has a lot of obstacles eg: diversity of the analysed variable, coding of variable, distribution of the variables, that author didn’t mention.


Ver. 264 – "In the other clusters, a normal distribution of prices was obtained by removing outlying data." The author should present the method to remove outliers. It should be highlited that outliers should be carefully treated due to the fact that their are the additional (precious) information about market.


Figure 3 – without georeferences and topographic information is not readable, and the draw conclusion is not possible.


Ver. 336 – "The method used guarantees that within each of the zones reliability in the estimated property value is greater than 90%."


The Author states this brave conclusion on the basis of the Table 6 where verified the proposed approach on the basis of the several percent of the observations. The approaches have been analysed on the basis of the one market and outdated database which is not very reliable.


The main conclusion is that the analyses are too modest and too simplified the domain of the analyses that is unusually complex. The interpretation of the area of market in the "continuous way" (Fig. 3) is very controversial on the basis of the sparse data points (transactions). It is possible but needs very sophisticated analyses and researches.


Although, the selected themes relevant to the wider international debate,  I can not recommend this paper to be published in the such recognized and scientific high level of Geosciences Journal.


Author Response

 

The author thank to the anonymous reviewer for their work and attention given on this paper.

I have got many deep and critical comments that helped in getting this article much better and will help us also in next research.

According to the editor suggestions the new parts of the article are written in red.


1.        The Author presented   research relevant to implementation automatic models to valuation real   estate. Generally, scope of the research is very up-to-date and lively in   wider international discussions. However, the presented specific results and   approach has been already well studied field and the analysis is very basic   and I cannot see any novelty. Moreover the presented findings consider by the   Author as a new or innovative where overthrown to some extent 15 years ago by   the researchers related to property market analyses.

       The elaborated approach is based on   the assumption that the non-spatial and spatial attributes of a property   should be analysed separately. As a result, a location-insensitive model is   developed in the first stage, based only on the properties’ structural   characteristics. Using data mining techniques, in particular k-means   clustering, clusters of similar properties creating submarkets are formed.   Each cluster is characterised by defined attribute values, expressed on a   rank scale. During the second stage of the elaborated approach,  a pure spatial model is developed, based on   the assumption that the value of a property in each of the clusters depends   exclusively on the distance between properties with known prices and the   property being evaluated.

       A   literature review shows that some authors have opted for the use of   geostatistical methods in mass appraisal (Basus, 2000), with some of them   using the segmentation method of property market analyses (Maclennan et al,   1996). In the geostatistical methods the simplest varieties of kriging have   been used. Cichociński (2009) carried out an attempt to apply the   geostatistical methods, namely simple kriging, to interpolate property   values, while Ligas (2009) as well as Colaco and Vucetic (2012) applied the   regression-kriging model, called the hybrid model, to estimate the value of   land. Other, more advanced methods of data processing and interpolation,   including data integration, such as cokriging, were used rarely, with a   publication of Chica-Olmo (2007) being one of the few.

           None of these methods has shown   sufficient accuracy to significantly affect the methods of property   evaluation. The application of the two-stage approach to mass valuation of   properties using the k-means method, and the geostatitical method in the   second stage, is a novel methodology, especially for local properties.

        

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal and geostatistics” equals 1 (from   1900 to 2019).

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal and k-means method grouping”   equals 1 (from 1900 to 2019).

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal, geostatistics, and k-means   method grouping” equals 0 (from 1900 to 2019).

2.        Ver.61. – „The   multidimensional nature of geographical space is usually ignored and the   impact of a property’s situation reduced to an analysis of its location and   neighbourhood, treated as environmental features. “

 

This statement seems to be misbelief. First it’s not true that   geographical space is usually ignored that even Author confirmed this fact by   presenting many authors that made an implementation of it. Furthermore, the   Author claimed that researchers take only into account location and neighbourhood   instead of nature of geographical space during the real estate market   analyses. The question is: what then the nature of geographical space is? To   differentiate it from  location and   neighbourhood?

The answer   to the doubts is in lines 340-345:

 

“In the hedonic model, the most commonly-used one for estimating property   values, spatial position (location) is taken into account indirectly, by   determining the accessibility and neighbourhood. Accessibility is measured as   distance from the centre, in line with the location theory developed by von   Thunen, and neighbourhood usually understood as the property’s purpose in the   land development plan. The proposed model takes into account location,   analysed in accordance with the rules of geostatistics and interpolation   using the kriging model.”

3.        Additionally the Author   actually didn’t even define any location feature or neighbourhood, and try to   classify real estate market taking into account just coordinates which is   illogical when we want to create the real estate valuation chart (consider   valuation map). It seems that Author didn’t consider fundamental knowledge   about the real estate market which is specific domain of the analyses in   which we can observe: uncertain, imprecision of property information, the   sudden and unpredictable changes, absence of homogenous functional   dependencies between real estate attributes, significant differences between   real estates etc.

 

            Kriging assumes that the distance   or direction between sample points reflects a spatial correlation that can be   used to explain variation in the surface. The Kriging tool fits a   mathematical function to a specified number of points, or all points within a   specified radius, to determine the output value for each location. Kriging is   a multistep process; it includes exploratory statistical analysis of the   data, variogram modelling, creating the surface, and exploring a variance   surface. 

             There is an assumption that when kriging is used there is no need to   define the location or neighbourhood characteristics which would be applied   to the grouping with the k-means method.

4.        Ver.77 – „The most   frequently used cartographic method are isolines or choropleth maps. „ and   ver. 83.

 

What is the basis of such statement?

           A thorough analysis of   cartographic presentation methods used to develop maps of property value was   conducted in the doctoral dissertation. The reference has been added in the   revised article.

5.        Ver. 88 – This is not new   approach, for sure. eg: Maclennan, D & Tu, Y 1996, 'Economic perspectives   on the structure of local housing systems' Housing Studies, vol. 11, no. 3,   pp. 387-406. and many others.

         Maclennan & Tu in the article   ‘Economic perspectives on the structure of local housing systems' examines   the notions of market and sub‐market in the context of housing. It first   proposes specific definitions and then clarifies why the general   characteristics of housing are likely to generate sub‐markets.

         In this article the author uses the   division into sub-market. The aim of the article is, however, to develop a   two-stage methodology for estimating property values using the k-means and   geostatic methods, which is a new approach.

         The number of articles indexed in WoS   and Scopus databases on “Mass appraisal, geostatistics, and k-means method   grouping” equals 0 (from 1900 to 2019).

6.        Ver.215 – in the   paper should be insert: the formula to update the transaction and   verification of the model. Moreover, the Author didn’t present the specific   information about the attributes and their domains. This has direct influence   on the selected method results and their quality etc.

 

 

        The table with attributes and its   domains is added on page 6 in the revised text. Additional information about the   methods of transaction prices updating is added on page 3 and 6.

7.        Ver. 221- one of the attributes is the “standard of   the flat”, as a matter of fact that kind of attribute does not exist in the national   property registry, so the question is: How the Author obtained this attribute   for 1873 transactions from 2007 -2011 years?

 

       The attribute was taken from the   Polish registry of property prices.

8.        Ver. 218. – the   verification of the significance the correlation results should be presented.

        The p-value was added to Table 2   (page 6).

9.        Ver. 228 – the   method of k-means clustering has a lot of obstacles eg: diversity of the   analysed variable, coding of variable, distribution of the variables, that   author didn’t mention.

 

          The author added the limitation of   k-means method on page 11 (line 355-360).

10.     Ver. 264 - “In the   other clusters, a normal distribution of prices was obtained by removing   outlying data. “ The author should present the method to remove outliers. It   should be highlited that outliers should be carefully treated due to the fact   that their are the additional (precious) information about market.

 

  

           Some more information on removing   outlying data was added.

           It is obvious that outliers   constitute additional information about the property market. However, in this   case typical properties were used only.

11.     Figure 3. –   without georeferences and topographic information is not readable, and the   draw conclusion is not possible.

               Figure 3 shows   kriging interpolation only. It doesn’t present maps of property values or any   elements from an additional map.

 

12.     Ver. 336 – “The method used guarantees that within   each of the zones reliability in the estimated property value is greater than   90%. The Author states this brave conclusion on the basis of the Table 6   where verified the proposed approach on the basis of the several percent of   the observations. The approaches have been analysed on the basis of the one   market and outdated database which is not very reliable.

 

                 The accuracy of   the property value estimates was checked using a test sample of 10% of the properties   not taken into account in the interpolation.

 

                   Further research will deal   with a verification of the method in another location of property market,   using different property data.

13.     The main conclusion is that the analyses are too   modest and too simplified the domain of the analyses that is unusually   complex. The interpretation of the area of market in the “continuous way”   (Fig. 3) is very controversial on the basis of the sparse data points   (transactions). It is possible but needs very sophisticated analyses and   researches.

 

                 The theoretical and minimum   number of properties on the basis of which interpolation with the kriging   method can be applied is 30. The analysis shows that for 90% reliability of   the property value estimate this number should be at least 200. It is also   necessary to  evenly include  the whole area. It is possible to obtain so   many data sets, especially in small towns, when data from multiple years are   analysed.

                Nearly 2000 transactions from   a 5-year time period have been used in the analysis.


Author Response File: Author Response.docx


Reviewer 4 Report


The paper overall is interesting and it refers to a topic, which is both timely and original. It is well - written and of good quality and presents a two-stage model for estimating the value of residential property, applied on a real-world case-study in Poland. The literature review part (introduction) could be enriched, e.g. see: Giannopoulou, M., Vavatsikos, V. and Lykostratis, K. (2016), A Process for Defining Relations between Urban Integration and Residential Market Prices, Procedia - Social and Behavioral Sciences, vol. 223, pp. 153-159. 


Moreover, a critical point is that the authors should provide more information at the results part, as the discussion right now is rather limited. The authors could also try to provide the reader with some comparative information, comparing the results of this case study to other case studies with similar characteristics worldwide in order to explore potential similarities and differences. Last but not least, the section “Summary and Conclusions” is currently only a summary – no major conclusions are drawn, the authors just repeat briefly what they did in this research. Therefore it is necessary that they move their analysis one step further and add the main lessons learnt after the application of their model to Siedlce, and also some ideas - perspectives of what would be an interesting continuation of this research.


Author Response

 

The author thank to the anonymous reviewer for their work and attention given on this paper.

I have got many deep comments that helped in getting this article much better and will help me also in next research.

According to the editor suggestions the new parts of the article are written in red.

The text was verified by native speaker, the English lecturer.


1.      The paper overall is   interesting and it refers to a topic, which is both timely and original. It   is well - written and of good quality and presents a two-stage model for   estimating the value of residential property, applied on a real-world case-study   in Poland. The literature review part (introduction) could be enriched.

The literature review was   enriched.

2.        Moreover,   a critical point is that the authors should provide more information at the   results part, as the discussion right now is rather limited.

The authors could also   try to provide the reader with some comparative information, comparing the   results of this case study to other case studies with similar characteristics   worldwide in order to explore potential similarities and differences.

A new chapter with discussion has   been added. The description   of the results has been expanded.

3.      Last but not least, the   section “Summary and Conclusions” is currently only a summary – no major   conclusions are drawn, the authors just repeat briefly what they did in this   research. Therefore it is necessary that they move their analysis one step   further and add the main lessons learnt after the application of their model   to Siedlce, and also some ideas - perspectives of what would be an   interesting continuation of this research.

The conclusions are expanded.


Author Response File: Author Response.docx


Round  2


Reviewer 3 Report


The author’s answer for the reviewer’s doubts are not satisfied. Reviewer aswers for author's comments - red fond.


1.        The Author presented research relevant to implementation automatic models   to valuation real estate. Generally, scope of the research is very up-to-date   and lively in wider international discussions. However, the presented   specific results and approach has been already well studied field and the   analysis is very basic and I cannot see any novelty. Moreover the presented   findings consider by the Author as a new or innovative where overthrown to   some extent 15 years ago by the researchers related to property market   analyses.

 

 

       The elaborated approach is based on   the assumption that the non-spatial and spatial attributes of a property   should be analysed separately. As a result, a location-insensitive model is   developed in the first stage, based only on the properties’ structural characteristics.   Using data mining techniques, in particular k-means clustering, clusters of   similar properties creating submarkets are formed. Each cluster is   characterised by defined attribute values, expressed on a rank scale. During   the second stage of the elaborated approach,    a pure spatial model is developed, based on the assumption that the   value of a property in each of the clusters depends exclusively on the   distance between properties with known prices and the property being   evaluated.

       A literature review shows that   some authors have opted for the use of geostatistical methods in mass   appraisal (Basus, 2000), with some of them using the segmentation method of   property market analyses (Maclennan et al, 1996). In the geostatistical   methods the simplest varieties of kriging have been used. Cichociński (2009)   carried out an attempt to apply the geostatistical methods, namely simple   kriging, to interpolate property values, while Ligas (2009) as well as Colaco   and Vucetic (2012) applied the regression-kriging model, called the hybrid   model, to estimate the value of land. Other, more advanced methods of data   processing and interpolation, including data integration, such as cokriging,   were used rarely, with a publication of Chica-Olmo (2007) being one of the   few.

           None of these methods has shown   sufficient accuracy to significantly affect the methods of property   evaluation. The application of the two-stage approach to mass valuation of   properties using the k-means method, and the geostatitical method in the   second stage, is a novel methodology, especially for local properties.

        

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal and geostatistics” equals 1 (from   1900 to 2019).

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal and k-means method grouping”   equals 1 (from 1900 to 2019).

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal, geostatistics, and k-means   method grouping” equals 0 (from 1900 to 2019).

Reviewer answer-second   review: Combining spatial and non-spatial approaches in property analyses are   definitely not innovative approach.  Look for example in papers in Real Estate   Management and Valuation(REMV) journal.

Author proved that mass appraisal and geostatistics did not exist in the   papers on WoS. It is misunderstanding. The author should look more broad   scope at property analyses and geostatistic or similar to this topics. Moreover   author probably misunderstood automated valuation methods and mass appraisals   which are not synonyms. Detecting author’s approach the reviewer could understood   that author propose mass appraisal procedure that has very complex procedure   and has big consequences regarding mistakes. This indicated that aim of study   is not clear . In my opinion the paper can be considered as a procedure of   property market analyses in investment advisory  instead of    mass appraisal procedure.

2.        Ver.61. – „The multidimensional nature of geographical space is usually   ignored and the impact of a property’s situation reduced to an analysis of   its location and neighbourhood, treated as environmental features. “

 

This statement seems to be   misbelief. First it’s not true that geographical space is usually ignored   that even Author confirmed this fact by presenting many authors that made an   implementation of it. Furthermore, the Author claimed that researchers take   only into account location and neighbourhood instead of nature of   geographical space during the real estate market analyses. The question is:   what then the nature of geographical space is? To differentiate it from  location and neighbourhood?

The answer   to the doubts is in lines 340-345:

 

“In the hedonic model, the most   commonly-used one for estimating property values, spatial position (location)   is taken into account indirectly, by determining the accessibility and   neighbourhood. Accessibility is measured as distance from the centre, in line   with the location theory developed by von Thunen, and neighbourhood usually   understood as the property’s purpose in the land development plan. The   proposed model takes into account location, analysed in accordance with the   rules of geostatistics and interpolation using the kriging model.”

Reviewer answer-second review: Not satisfied answer. Why better is using spatial   position then distances or positioning. What are disadvantages of second one?

3.        Additionally the Author actually didn’t even define any location feature   or neighbourhood, and try to classify real estate market taking into account   just coordinates which is illogical when we want to create the real estate   valuation chart (consider valuation map). It seems that Author didn’t   consider fundamental knowledge about the real estate market which is specific   domain of the analyses in which we can observe: uncertain, imprecision of   property information, the sudden and unpredictable changes, absence of   homogenous functional dependencies between real estate attributes,   significant differences between real estates etc.

            Kriging assumes that the distance   or direction between sample points reflects a spatial correlation that can be   used to explain variation in the surface. The Kriging tool fits a   mathematical function to a specified number of points, or all points within a   specified radius, to determine the output value for each location. Kriging is   a multistep process; it includes exploratory statistical analysis of the   data, variogram modelling, creating the surface, and exploring a variance   surface. 

             There is an assumption that when   kriging is used there is no need to define the location or neighbourhood   characteristics which would be applied to the grouping with the k-means   method.

Reviewer answer-second   review: Not satisfied eg.: mentioned ”variation in the surface” what does it mean regarding  differentiation of the property in the space?   Look at previous question as well.

4.        Ver.77 – „The most frequently used cartographic method are isolines or choropleth   maps. „ and ver. 83.

 

What is the basis of such   statement?

           A thorough analysis of   cartographic presentation methods used to develop maps of property value was   conducted in the doctoral dissertation. The reference has been added in the   revised article.

Reviewer answer-second review: Not satisfied . The   author quoted just herself

5.        Ver. 88 – This is not new approach, for sure. eg: Maclennan, D & Tu,   Y 1996, 'Economic perspectives on the structure of local housing systems'   Housing Studies, vol. 11, no. 3, pp. 387-406. and many others.

 

 

 

 

         Maclennan & Tu in the article   ‘Economic perspectives on the structure of local housing systems' examines   the notions of market and sub‐market in the context of housing. It first   proposes specific definitions and then clarifies why the general   characteristics of housing are likely to generate sub‐markets.

         In this article the author uses the   division into sub-market. The aim of the article is, however, to develop a   two-stage methodology for estimating property values using the k-means and   geostatic methods, which is a new approach.

         The number of articles indexed in   WoS and Scopus databases on “Mass appraisal, geostatistics, and k-means   method grouping” equals 0 (from 1900 to 2019).

Reviewer answer-second review: Not satisfied. Look at   eg.: REMV numerous  papers about this   topic.

The mentioned research just proved that similar analyses where considered   more than 20 years ago.

6.        Ver.215 – in the paper should be insert: the formula   to update the transaction and verification of the model. Moreover, the Author   didn’t present the specific information about the attributes and their   domains. This has direct influence on the selected method results and their   quality etc.

 

        The table with attributes and its   domains is added on page 6 in the revised text. Additional information about the   methods of transaction prices updating is added on page 3 and 6.

Reviewer answer-second review: Not   satisfied. Still no formulas and analyses of the significance of the model is   provided.

7.      Ver. 221- one of the   attributes is the “standard of the flat”, as a matter of fact that kind of   attribute does not exist in the national property registry, so the question   is: How the Author obtained this attribute for 1873 transactions from 2007   -2011 years?

 

       The attribute was taken from the   Polish registry of property prices.

Reviewer answer-second   review: Not satisfied. No answer for   the “standard” variable. Registry of property prices collected data from notarial   deed, where no standard is descripted.

8.        Ver. 218. – the verification of the significance the   correlation results should be presented.

        The p-value was added to Table 2   (page 6).

Reviewer answer-second   review: Approved

9.        Ver. 228 – the method of k-means clustering has a   lot of obstacles eg: diversity of the analysed variable, coding of variable,   distribution of the variables, that author didn’t mention.

 

          The author added the limitation of   k-means method on page 11 (line 355-360).

Reviewer answer-second   review: Approved (although the k-means has much more disadvantages)

10.    Ver. 264 - “In the other clusters, a normal distribution of   prices was obtained by removing outlying data. “ The author should present   the method to remove outliers. It should be highlited that outliers should be   carefully treated due to the fact that their are the additional (precious)   information about market.

 

  

           Some more information on removing   outlying data was added.

           It is obvious that outliers   constitute additional information about the property market. However, in this   case typical properties were used only.

Reviewer answer-second   review: Approved

11.      Figure 3. – without georeferences and topographic   information is not readable, and the draw conclusion is not possible.

 

 

               Figure 3 shows kriging   interpolation only. It doesn’t present maps of property values or any   elements from an additional map.

 

Reviewer answer-second   review: So then for who and for what  this   Figures where prepared considering the scope of the analyses?

12.    Ver. 336 – “The method used   guarantees that within each of the zones reliability in the estimated   property value is greater than 90%. The Author states this brave conclusion   on the basis of the Table 6 where verified the proposed approach on the basis   of the several percent of the observations. The approaches have been analysed   on the basis of the one market and outdated database which is not very   reliable.’

 

 

                 The accuracy of the property   value estimates was checked using a test sample of 10% of the properties not   taken into account in the interpolation.

 

                   Further research will deal   with a verification of the method in another location of property market,   using different property data.

Reviewer answer-second   review: Not satisfied. It should be   verified with more observations. The results seems to be unreliable   especially if the author consider this method in mass appraisal context. No   formula to verify the results were delivered.

13.    The main conclusion is that   the analyses are too modest and too simplified the domain of the analyses that   is unusually complex. The interpretation of the area of market in the   “continuous way” (Fig. 3) is very controversial on the basis of the sparse   data points (transactions). It is possible but needs very sophisticated   analyses and researches.

 

                 The theoretical and minimum   number of properties on the basis of which interpolation with the kriging   method can be applied is 30. The analysis shows that for 90% reliability of   the property value estimate this number should be at least 200. It is also   necessary to  evenly include  the whole area. It is possible to obtain so   many data sets, especially in small towns, when data from multiple years are   analysed.

                Nearly 2000 transactions from   a 5-year time period have been used in the analysis.

Reviewer answer-second   review: More analyses and area should   be considered to prove the obtained findings.


Author Response

The author would like to thank the anonymous reviewer for their work and attention given to this paper. Many deep and critical comments have been used to improve this article and will be also used in the next research.

Please see the detailed point-by-point coverletter in the attachement.


Author Response File: Author Response.docx


Round  3


Reviewer 3 Report


Thank you for the answers.


Author Response


The author would like to thank the anonymous reviewer for their work and attention given to this paper.

The sentences in lines 10  and  95 have been revised. The sentence in line 106 has been removed.

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