The present invention refers to a process and a system for monitoring and economically evaluating real estates.

It is known that the process for economically evaluating a certain real estate requires a high amount of preparation searches and an accurate and complex analysis of available data related to such estate. In spite of this, often traditional evaluation methods are mostly based on subjective considerations about the different premium or negative parameters that affect the real estate value and therefore cannot be implemented in a completely automatic and statistically meaningful way.

In order to solve such inconvenience, the art has proposed evaluation methods that could be implemented in a more and more automatic and subjective way. Examples of such evaluation methods are disclosed, in particular, in US2009198681, KR20090015120, US2010042446, JP2009251986, JP2009211324, JP2008165764, JP2008071 100, JP2007156676, JP3768515, JP2006085501.

Even such methods however still suffer from several inaccuracy levels about the exact quantification of parameters affecting the final estate value, thereby providing final results affected from very wide confidence ranges, above all when a single estate has to be evaluated.

Therefore, object of the present invention is solving the above prior art problems by providing a process for monitoring, analysing and meaningfully synthesizing real estates values that is an alternative to what has been proposed by the prior art, that allows, in particular, knowing the market value of such real estates with an increasing accuracy level, starting from zone values and going up to a statistically meaningful market value for the individual real estate being evaluated.

Another object of the present invention is providing a process for economically evaluating real estates using received real estate values through databases of real estates and surveys about homogeneous areas, returning a price range from minimum to maximum for real estates that can be found on a certain territory.

Moreover, an object of the present invention is providing a system for economically evaluating real estates implementing the above process.

The above and other objects and advantages of the invention, as will result from the following description, are obtained with a process for economically evaluating real estates as claimed in claim 1.

Moreover, the above and other objects of the invention are obtained with a system for economically evaluating real estates as claimed in claim 12.

Preferred embodiments and non-trivial variations of the present invention are the subject matter of the dependent claims.

It is intended, that all enclosed claims are an integral part of the present specification.

It will be immediately obvious that numerous variations and modifications (for example related to shape, sizes, arrangements and parts with equivalent functionality) could be made to what is described, without departing from the scope of the invention as appears from the enclosed claims.

The present invention will be better described by some preferred embodiments thereof, provided as a non-limiting example, with reference to the enclosed drawings, in which:

Figure 1 shows a flow diagram related to a step of a preferred embodiment of the process according to the present invention;

- Figure 2 shows a flow diagram related to another step of a preferred embodiment of the process according to the present invention;

Figure 3 shows an example image related to a computing step of the process according to the present invention;

Figure 4 shows a block diagram that schematically shows a preferred embodiment of the system according to the present invention.

In general, as can be seen herein below in more detail, the process according to the present invention provides for the aggregation of real estate evaluations and sales-purchases performed by professionals in the real estate market in a certain period of time and in a specific geographical surrounding, with the objective ot determining the market value of a particular zone and, when possible, of a particular street, square, etc.

Therefore, with reference to Figures 1 to 3, it is possible to note that the process for economically evaluating real estates according to the present invention comprises:

- at least one first batch step Fl for pre-processing data present on at least one database of real estates for providing pre-processed data adapted to be used in a following step of on-line computing an economic value of at least one certain real estate;

at least one second on-line step F2 for computing and providing, preferably through a Web Service (WS) to at least one caller application, an evaluation of the mean economic value of at least one of such real estates in a certain geographic zone depending on such pre-processed data; and

alternatively, a third step adapted to determine and locate homogeneous geographic areas, for example through known processes of Geographic Space Cluster, through which it is possible to select at least one sample comprising only real estates comparable and pertaining to the above certain real estate and define value interpolation bands, or a third step of using predefined homogeneous zones..

The above steps of the process according to the present invention therefore aim, in particular, to geo-locate a geographic location of the above certain real estate in terms of complete address (in terms, for example of pieces of information such town, street, street number) and afterwards to extract one of such samples of comparable real estates present in the surroundings of such geographic location. Finally, as can be seen below in more detail, the thereby extracted sample will be used, after suitable amendments and parametrisations, for computing a confidence range, made for example on a Student distribution t, with a certain probability.

The process according to the present invention could further comprise a another step of enlarging the statistical base of the above data due to an integration with other real estates databases.

With reference in particular to Figure 1, it is possible to note that the first step Fl comprises the steps of:

providing F101 such data from such at least one database of real estates, preferably in at least one first pre-set data structure like the one shown, for example, i the following Table 1 : Name Type Comments

Address Text

Category Text

Stair Text

Maintenance status Text

Heating Type Text

Commercial Square Numeric

Meters

Conditioned Air Boolean

Purchase Date Date

Sales Date Date

End-of-Mandate Date

Date

Initial Economic Numeric

Sales Value

Last Economic Numeric

Value

Evaluation Numeric

Sales Economic Numeric

Value

Longitude Numeric

Latitude Numeric

Table 1 : data structure of the real estates geo-locating F103 at least one of such real estates; preferably, such step provides that all addresses of such real estates are subjected to a Geographic Information System ("GIS") for geo-coding, saving the results in a second pre-set data structure, such as, for example, the one shown in the following Table 2:

Area Trend Numeric

Category n

Demand Trend Numeric

Mean sales times Numeric

Table 2: data structure of homogeneous areas

processing F105 such collected data for homogeneous areas from estate agents and computing a trend of at least one of such areas; - processing and analysing F107 the expense availability data arrived at estate agents about such homogeneous areas; verifying F109 the coherence of such data: in particular, such step preferably provides that all real estates are discarded that: a) it has not been possible to geo-locate; and/or b) are placed at more than a parametric distance from the designated place; and/or c) have a value per square meter that is higher than a basic parameter of the category to which they belong; processing Fi l l such real estates data for such homogeneous areas (whose geometry is known) for determining the mean offset of the economic value depending on one or more corrective parameters, such as for example, category and/or stair of such real estate; processing Fl 13 mean sales times data of real estates on such homogeneous areas: in fact, the mean sales time is an important indicator of the real estate market trend and can be computed as the time passing between the acquisition date of the sales mandate and the sales date of the real estate itself. A further indication for such purpose can be provided by the length of time of stay of the sales advertising of a real estate on Internet; and providing Fl 15, preferably upon request, such data to such second step F2 of the process according to the present invention.

In particular, such step F107 allows verifying which is the expense availability in a certain geographic area with Latitude A and Longitude B and how it evolved upon the passage of time. If Point A-B falls inside the homogeneous area, it will be possible to compute a moving mean (monthly, quarterly, every six months, yearly, etc.) of the expense availability of purchasers for a certain estate type (single room, two rooms, three rooms, etc.): an historical series is thereby obtained with the mean expense availability, from which it is possible to obtain market trends, understand whether the expense availability of purchasers is growing, stable or decreasing. Data are present in a database and are univocally associated to real estate agencies present in the territory.

Data for implementing such step 107 need a preventive cleaning before being used: for example, let in fact X be the mean of all expense availabilities recorded upon time by a specific real estate agency; then:

Xmax = X ^■ 2 = Maximum Limit of Expense Availability mm = X ^■ 0, 5 = Minimum Limit of Expense Availability

All values falling outside the range { ^x min' ^{" x} _m -o Lj i) will have to be excluded from the sample.

Now, from the sample cleaned from too high or too low values, it will be possible to compute the following series:

mean of expense availability in period 1 mean of expense availability in period 2 ^x n = mean of expense availability in period n

For example, the following mean availability upon time is given in a homogeneous area of Genoa:

X = 152.687, 5€ = Mean of Expense Availability then:

Xmax = X · 2 = 152.687, 5 · 2 = 305. 375€ = Maximum Limit of Expense availability Xmin = X ^* 0 5 = 76. 334€ = Minimum Limit of Expense availability from which: Validity Range of Expense Availability After having cleaned the sample from values outside the range and taking into account a six-month time horizon, the following Table 3 is obtained, with data related to the expense availability:

Table 3: Expense availability data Obviously, the above series can also be displayed in a graphic form, including time in the abscissas and values of mean expense availability in the ordinates.

In particular, such step Fi l l firstly provides to determine, for a certain geographic area (area with homogeneous values), at least one Corrective Parameter (PC) related to an Intrinsic Real Estate Characteristic (CII) of a real estate such as, for example, an estate building. The PC can then increase or decrease the mean estate economic value of the relevant geographic area. The main Increasing Corrective Parameters (PCM) can be those listed below, but the list can obviously be expanded depending on uses and habitudes that can be detected for every homogeneous area being surveyed:

premium view;

panoramic view;

relevant services nearby;

floor position (top/bottom);

- other CII.

The main Decreasing Corrective Parameters (PCP) can be the following, also obviously adapted to be modified depending on uses and habitudes of the geographic area being surveyed:

negated view;

- noisy/polluting sources nearby;

noisy/polluting sources in front;

absence of common technologic plants;

floor position (top/bottom);

other CII.

The survey area can be determined in order to point out a homogeneous range of values per square meter, taking into account real estate units with standard CII. Within the survey area being defined, the real estates equipped with Standard Intrinsic Estate Characteristics can be located, namely those real estates that have no positive or negative characteristics. From these, a range of values per square meter will be computed, for example with the same computation modes that will be described below for the statistical computation of the minimum economic value and of the maximum economic value of a real estate. Inside the defined area, a number n of real estate units can be located, for the search of each PCM and for each PCP, characterised in that they have a single CII for which the PC is searched. The ratio between the lower end of the confidence range of values per square meter of the Standard Real Estate and the lower end of the confidence range of the real estates with single CII, will give the lower value of the percentage range, for each PCP/PCM. The ratio between the upper end of the confidence range of the values per square meter of the Standard Real Estate and the upper end of the confidence range of the real estates with single CII, will give the lower value of the percentage range, for each PCP/PCM.

Herein below, a computation example will be given, which implements such step Fl 11. For the example purposes, the PCP = "noisy/polluting sources in front" is taken into account, but obviously what is described above is valid and can be extended for all other PCP/PCM.

The reference homogeneous area of the real estate PCP is then located, with homogeneous market values per square meter. With particular reference to Figure 3, on a satellite geographic map M showing such homogeneous survey area A, the real estates with Standard Estate Parameters with PIS indication are pointed out, while the real estates with PCP = "Noisy/polluting source in front" (for example a large street C with a lot of traffic) with PCP indication are pointed out. For everything, the economic value per square meter is computed.

After having applied a statistical computation method of the minimum economic value and of the maximum economic value of a real estate as below, the 95% confidence range is determined for the value per square meter of the area in which the real estate PIP falls. The real estates taken into account for such computation are those with Standard Intrinsic Parameters The 95% statistical confidence range points out a price range of 2,070 - 2,330€/sqm, with a mean of 2,200€/sqm.

The same computation is performed for the real estates with the same

Intrinsic Real Estate Characteristic for which the PC is searched. The 95% statistical confidence range points out a price range of 1 ,502 - 1,998€/sqm, with a mean of 1,750€/sqm.

The relevant Decreasing Corrective Parameter, equal to "Noisy/polluting source in front", is computed on the ends of the confidence range as follows:

lower end: 1 -(1 ,502 / 2,070)* 100 = 27.4%

upper end: 1 - (1,998/,.330)* 100 = 14.3%

The thereby computed range 14.3% - 27.4% is the range within which the PCT of the zone for the relevant Intrinsic Real Estate Characteristic is defined. This PCP will be the discount coefficient, to be applied to the zone mean economic value, in the evaluation of a real estate that is in front of a noisy or polluting source. The parameter will be chosen next to one or the other end of the range, the CUU of the real estate being evaluated being evident or not.

With particular reference now to Figure 2, it is possible to note that the second step F2 of the process of the present invention comprises the steps of: providing F201 geographic location data of at least one real estate being evaluated (such as, for example, address and/or geographic coordinates) and a set of parameters PI, P2, P3, P4, where PI is the value of the radius of a circumference whose centre is the point located by the above geographic location data, P2 is the number of real estates contained in a minimum radius Rl, P3 is the number of real estates contained in a minimum radius R2 and P4 is a comparison value of such minimum radius R2: possibly, one or more optional data can be provided, such as real estate category, etc.;

locating F203 from such database of real estates all those estates being present within a radius equal to PI from the point located by such geographic location data and included in the homogeneous zone where said real estate is present; from such step Fl 15, extracting F205 data related to the homogeneous area in which there is the real estate defined by such geographic location data; possibly, one or more optional data can also be extracted F207, such as, for example, medium sales time and expense availability;

locating F209 a first minimum radius Rl containing at least one number P2 of real estates and at least one second minimum radius R2 containing at least one number P3 of real estates all belonging to the same category: such step can be implemented through.any "backtracking" algorithm, known per se;

- standardising F211 the above data so that, if P4 < R2, then the real estates contained within the first radius Rl are taken into account, standardising them with the area parameter in order to cancel the category difference; otherwise, the real estates contained within the second radius R2 are taken into account, related only to the required category, and standardising the real estates values depending on the parameters of the homogeneous area; statistically computing F213 the minimum economic value and the maximum economic value of such real estate; and

providing F215 the results of the above computation and, possibly, optional data and parameters of the homogeneous area.

As an example, herein below a preferred method is included for the statistical computation of the minimum economic value and of the maximum economic value of a real estate that can be implemented in the above steps Fi l l and F213.

To obtain the minimum economic value and the maximum economic value per square meter of a certain type of real estate in a certain geographic location, it is necessary to have available a sample composed of N values of prices and evaluations. Designating with A the Latitude and with B the Longitude of such geographic location of the real estate for which a real estate evaluation is being searched, such sample of numerosity N is composed of:

a) nl possible prices per square meter present in the above database of real estates and located in the same geographic location of the real estate for which the evaluation is searched (for example, same street, same main street, same square, etc.). Such prices, that will be part of the sample, must be located within the circle with centre A-B and radius R (the length of R changes according to the geo- localisation process); _,

b) n2 possible evaluations per square meter present in the database of real estates and located in the same geographic location of the real estate for which the evaluation is searched (same street, same main street, same square, etc.). The evaluations that will be part of the sample must be located within the circle with centre A-B and radius R (the length of R changes according to the geo-localisation process); c) n3 possible prices per square meter present in the database of real estates and located in a different geographic location of the real estate for which the evaluation is searched but placed within the circle with centre A-B and radius R (the length of R changes according to the geo-localisation process);

d) n4 possible evaluations per square meter present in the database of real estates and located in a different geographic location of the real estate for which the evaluation is searched but placed within the circle with centre A-B and radius R (the length of R changes according to the geo-localisation process) and at the same time within an homogeneous area;

e) n5 possible economic value per square meter present in the homogeneous area and referred to the zone containing point A-B;

f) n6 possible prices and/or evaluations coming from other database of real estates referred to a zone that contains point A-B.

Then the numerosity of sample N is given by:

N=nl +n2+n3+n4+n5+n6

where: nl>0; n2>0; n3>0; n4>0; 0<n5<l ; n6>0, with the following constraints:

constraint vl : if nl>3 then the sample is meaningful;

constraint v2: if nl<3 the sample is meaningful if and only if N>X (where X is a value to be established >3).

Some weights pi, p6 (that can be modified) are applied to the above sample of numerosity N, in order to assign greater or lesser importance to the variables composing the sample itself:

The numerosity of the weighted sample Np will therefore be obtained, given by:

Np=nl ·ρ1+ ^η 2·ρ2+η3·ρ3+η4·ρ4+η5·ρ5+η6·ρ6 where: pl=weight given to prices of item a); p2=weight given to evaluations of item b); p3=weight given to prices of item c); p4=weight given to evaluations of item d); p5=weight given to the economic value of item e); p6=weight given to prices and/or evaluations of item f) with the following constraint v3: if nl>3 then N=nl -pl+n2-p2+n5-p5+n6-p6.

It must be noted that constraint v3 allows obtaining more accurate results if there are at least three prices that are actually in the geographic location being searched. In fact, n3 and n4 will be removed from the sample, n3 and n4 making the sample more heterogeneous and less accurate.

EXAMPLE 1

Let nl=l, n2=2, n3=6, n4=9, n5=l, n6=0; moreover, assuming that pl=4, p2=3, p3=2, p4=l, p5=2, p6=l, then:

Νρ= ^η 1·ρ1+ ^η 2·ρ2+ ^η 3·ρ3+ ^η 4·ρ4+ ^η 5·ρ5+ ^η 6·ρ6=1 ·4+2-3+6·2+9·1+1 ·2+0·1=4+6+12+9 +2+0=33.

EXAMPLE 2

Let nl=4, n2=2, n3=7, n4=8, n5=l, n6=0; moreover, assuming that pl=4, p2=3, p3=2, p4=l, p5=2, p6=l, then, depending on constraint v3, there will be: Νρ=η1 ·ρ1+η2·ρ2+ η5·ρ5+η6·ρ6=4·4+2·3+1 -2+0-1=16+6+2+0=24.

Prices and evaluations being present in the weighted sample will be used for computing Confidence Ranges that return a minimum economic value and a maximum economic value associated with an established probability a and a geographic area.

It is known that the Confidence Range for the mean of a population (with unknown μ and σ) is given by:

that, in case of the process according to the present invention, will be:

¾ = Minimum Price per sqm

i*z = Maximum Price per sqm

X = Mean of Weighted Sample ^f» - ⁱ ¾ = Tabulated value of the Student distribution t for a sample with n-1 degrees of freedom and probability ^a

s = standard deviation estimation

n = numerosity of weighted sample

Considering that the prices confidence range is built on the mean of a weighted sample and not on the mean of the original sample, it is therefore necessary to verify with an Hypothesis Test, the null hypothesis H ₀ = Weighted Mean against the alternative hypothesis ¾ * Weighted Mean.

The significativity level is a (to be defined).

The critical region is built by assuming that prices and evaluations of the original sample are normally distributed and have unknown variance. The Test statistics T, under hypothesis Ho is distributed as a Student t with n-1 degrees of freedom.

The Test is valid if H ₀ = Weighted Mean falls outside the Critical Region: #o ^: = Ho = Mean of Weighted Sample

#i ^: P ^≠ Wo = Mean of Weighted Sample

^T=~ Vn"T = Test Statistics

** F P * .*} - Critical Region wheree: ^1 = Mean of original sample

s% = Standard deviation of original sample

nt = numerosity of original sample

If #o falls outside the critical region, it is possible to use the weighted sample instead of the original sample, without having negative effects on the final computation result of minimum and maximum economic values. Hypothesis Tests similar to the one described above can be used for verifying any other parameter that has to be used in this computation priocess. For example, an Hypothesis Test can be performed on the economic value of a certain homogeneous area to establish if it is coherent with prices and evaluations obtained from the database of real estates.

It is wholly clear that the present invention further refers to at least one computer program comprising computer program code means adapted to perform all or part of the steps of the above described process when such program is run on a computer.

The present invention further deals with a system 1 for economically evaluating real estates that allows implementing the process according to the present invention as previously described. In particular, as can be noted as an example from Figure 4, the system 1 according to the present invention comprises:

means 3 for storing at least one database of real estates;

- first processing means 5 adapted to pre-process data present at least on such database of real estates for providing pre-processed data, such first processing means 5 being in particular adapted to geo-locate at least one of such real estates, process such collected data for homogeneous areas from estate agents and compute a trend of at least one of such areas, verify the coherence of such data, process such data of real estates for such homogeneous areas to determine the mean offset of the economic value depending on one or more corrective parameters, process data of mean sales times of real estates on such homogeneous areas;

at least one remote device 7 equipped with at least one caller application that requires on-line, through at least one Web Service (WS) interface 9 resident on a web server, an economical evaluation of at least one real estate identifed through a geographic location thereof;

second processing means 11 adapted at least to statistically compute an economic value of such real estate at least depending on such geographic location.