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Zašto je važan kvalitet podataka u analizi nekretnina

Bez obzira da li se koristi tehnologija ili se procena radi na tradicionalan način nedostatak podataka i kvalitet podataka predstavlja ozbiljan izazov. 


Nekretnine nisu standardizovan proizvod, a broj karakteristika koje se moraju uzeti u obzir je veoma veliki. U analizu se uključuju fizičke karakteristike, lokaciju, sve stavke prihoda i rashoda, analiza zakupa, naknade, klauzule u ugovorima o zakupu i niz drugih faktora. Takođe uključuje se i analiza makro i mikro tržišta. Sve ove nijanse utiču na to da je veoma teško uporediti bilo koje dve nekretnine zbog količine potencijalne varijanse u svakoj osobini. Ova prepreka postoji bez obzira na broj nekretnina čijim podacima se može pristupiti. 


U praksi, zbog geografskih, vremenskih, kvalitativnih i pitanja transparentnosti podataka o nekretninama, broj nekretnina dostupnih za uporedivu analizu je izuzetno mali.


U poređenju sa drugim industrijama i delatnostima, količina podataka dostupnih za analizu nekretnina je ograničena. Osim toga da bi analiza bila moguća i tačna podaci moraju biti geografski relevantni i određeni, vremenski određeni i objektivni u pogledu kvalitativnih karakteristika i transparentni.

Why Quality of Data Matters in Real Estate Analytics

Regardless of whether we use technology or assessment is done in the traditional way, lack of data and data quality is a serious challenge.


Real estate is not a standardized product, and the number of characteristics that must be considered is exceptionally large. The analysis includes physical characteristics, location, all items of income and expenses, analysis of leases, fees, clauses in lease agreements and a number of other factors. Macro and micro market analysis is also included. All these nuances make it exceedingly difficult to compare any two properties due to the amount of potential variation in each characteristic. This barrier exists regardless of the number of properties that can be accessed.


In practice, due to geographical, temporal, qualitative and transparency of real estate data issues, the number of properties available for comparative analysis is extremely small.


Compared to other industries and activities, the amount of data available for real estate analysis is small. In addition, for the analysis to be possible, accurate data must be geographically relevant and specific, timed and objective in terms of qualitative characteristics and transparent.


Što je geografska oblast uža, generalno je manji broj uporedivih nekretnine koje se mogu koristiti u analizi. Pogotovo ako uzmemo u obzir da se za poređenje koriste transakcije nekretnina istog tipa i sličnih kvalitativnih karakteristika. Sužavanjem kriterijuma broj adekvatnih nekretnina za poređenje često se svodi na veoma mali broj.


Vremenska određenost

Ako procenjujemo nekretninu danas, podaci o prodajama sličnih nekretnina koje su se desile pre pet godina, a ponekad i pre godinu dana nisu adekvatni za analizu. Analiza mora da se ograniči na transakcije koje su se dogodine nedavno i pod sličnim tržišnim uslovima. Svako uključivanje podataka koji ne zadovoljavaju ova dva uslova zahteva uvođenje pretpostavki što utiče na objektivnost i tačnost analize. Istorijske cene transakcija vremenom postaju manje reprezentativne, posebno kada su oscilacije na tržištu učestale.

Kvalitativne karakteristike

Kvalitativni podaci se odnose na informacije koje su subjektivne, kao što je vrednovanje orijentacije nekretnine, lokacije, površine, strukture, kvaliteta ugrađene opreme, održavanje, postojanje lifta, vrsta grejanja, oštećenja i slično. Nedostatak kvalitativnih podataka utiče na to da je rezultat analize često netačan.


Iako je čoveku lako da napravi razliku između dobrih i loših karakteristika nekretnine, skoro je nemoguće da računar uradi istu stvar. Iako čovek može da napravi takvu razliku, dva procenitelja će često različito vrednovati nepokretnost na osnovu istih karakteristika. Ovo važi za sve kvalitativne karakteristike nekretnina.


Transparentnost podataka

Detaljna analiza bilo koje komercijalne nekretnine uključuje pregled ne samo njenih fizičkih karakteristika, već i detaljnih finansijskih aspekata kao što su novčani tok i rente. Problem je, međutim, što ove informacije nisu javno dostupne za većinu nekretnina.


Deo relevantnih podataka za analizu nekretnine može se pronaći na internetu ili preko drugih javno dostupnih izvora. Ostatak informacija obično ima samo vlasnik nekretnine i te informacije nije moguće uključiti u analizu. Naravno, mogu se napraviti pretpostavke, ali kao što svako ko je ikada procenjivao komercijalnu nekretninu zna, male promene u prihodima ili rashodima mogu imati relativno značajan uticaj na procenjenu vrednost nekretnine.


The narrower the geographical area, the smaller the number of comparable properties that can be used in the analysis. Especially if we take into account that real estate transactions of the same type and similar qualitative characteristics are used for comparison. By narrowing the criteria, the number of adequate properties for comparison is often reduced to an exceedingly small number.

Temporal limitations

If we evaluate real estate today, the sales data of similar real estate that happened five years ago, and sometimes a year ago, are not adequate for analysis. The analysis must be limited to recent transactions which happened under similar market conditions. Any inclusion of data that does not meet these two conditions requires the introduction of assumptions that affect the objectivity and accuracy of the analysis. Historical transaction prices become less representative over time, especially as market fluctuations have become more frequent.

Qualitative characteristics

Qualitative data refers to information that is subjective, such as evaluation of the orientation of the property, location, area, structure, quality of equipment and materials, maintenance, existence of an elevator, type of heating, damage, and the like. The lack of qualitative data often leads to inaccurate analysis results.


Although it is easy for a person to distinguish between the good and bad characteristics of a property, it is almost impossible for a computer to do the same thing. Although one can make such a distinction, two appraisers will often evaluate real estate differently based on the same characteristics. This applies to all qualitative characteristics of real estate.

Data transparency

A detailed analysis of any commercial property includes an overview not only of its physical characteristics, but also of detailed financial aspects such as cash flow and leases. The problem, however, is that this information is not publicly available for most properties.


Some of the relevant data for real estate analysis can be found online or through other publicly available sources. The rest of the information is usually known only by the property owner and this information cannot be included in the analysis. Of course, assumptions could be made, but if you ever valued a commercial property, you know that minor changes in income or expenses can have a significant impact on the appraised value of a property.

Automatizovani modeli procene

Data Analytics

Automatizovani modeli procene (AVMs) koriste hedonistički model i indekse u svojim algoritmima. Dobar AVM ima pristup svim informacijama relevantnim za predmetnu nekretninu, kao što su istorija prodaje i podaci o prodaji za druge slične nekretnine.


Efikasnost procesa zavisi od podataka koji se koriste, jer se samo visokokvalitetni podaci zaista mogu smatrati pouzdanim i reprezentativnim. Međutim, važno je napomenuti da postoji ograničenje jer AMV isključuje mogućnost terenske inspekcije, što daje prednost tradicionalnom načinu procene, jer se mogu uočiti nedostaci koji inače nisu dokumentovani u podacima.


Kao i sa svakim novim alatom, važno je razumeti kako taj alat funkcioniše i koja mogu biti ograničenja. Cilj je svakako razvoj tačnog automatizovanog modela procene. Precizna automatizovana procena otvorila bi beskonačne mogućnosti. i to zaista zvuči neverovatno, možda malo i previše neverovatno da bi bilo istinito.


Kao opšte pravilo, procenitelj treba da bude svestan da je bilo koje analitičko sredstvo pouzdano samo onoliko koliko su kvalitetni podaci koji se unose u njega i analitički model koji se koristi.


Kako se softveri razvijaju i algoritmi unapređuju, stručnjaci za nekretnine će se sve više oslanjati na njih. Prednosti ovih rešenja su već sada očigledne, i omogućavaju da se process analize i procene u velikoj meri pojednostavi. Postojeći problem nedostatka fizičke posete nekretnine i nedostatak kvalitativnih podataka o nekretnini mogao bi da se prevaziđe uključivanjem procenitelja u proces ali to onda ne bi značilo da je process potpuno automatizovan i prepušten tehnologiji.


AVM procene sadrže greške iz više razloga, uglavnom zbog problema sa modelom i podacima, što se najčešće odnosi na sledeće nedostatke: model nije adekvatan, tržište nije statično i AVM model ne može da isprati promenjene uslove, kvalitet i obim podataka nije zadovoljavajuć, nedostatak fizičke inspekcije, zastarelost podataka.

Veštačka inteligencija

Netransparentna priroda podataka o nekretninama, posebno podataka o komercijalnim nekretninama, smatrana je izazovom za automatizovane modele procene, koji najbolje funkcionišu sa širokim i detaljnim bazama podataka. Međutim, promena u brzini i sofisticiranosti alata zasnovanih na veštačkoj inteligenciji, sposobnih da rade na ovim izazovima, zajedno sa povećanjem broja javnih baza podataka, promenila je igru.


Veštačka inteligencija mogla bi da poboljša efikasnost i tačnost modela automatizovane procene koji se gotovo uvek oslanja na standardnu hedonističku regresiju.


Koja je prednost primene veštačke inteligencije u analizi i proceni nepokretnosti?


Veštačke inteligencije može da prikupi podatke iz više javnih baza podataka i evidencija i u nekoliko sekundi pregleda milione dokumenata, pa čak može i da prikupi podatke koji nisu deo centralizovanih javnih baza podataka. Takođe, sistem koji uči iz raspoloživih podataka ima mogućnost da predviđa buduće trendove.


Koji god alat da se koristi u analizi, od kvaliteta podataka zavisi koliko će rezultat biti pouzdan, a samim tim i koliku težinu treba dati rezultatima u donošenju odluka.

Automated valuation models

Automated valuation models (AVMs) use hedonistic models and indexes in their algorithms. A good AVM has access to all information relevant to the property in question, such as sales history and sales data for other comparable properties.


The efficiency of the process depends on the data used, because only high-quality data can really be considered reliable and representative. However, it is important to note that there is a limitation because AMV excludes the possibility of field inspection, which gives preference to the traditional method of assessment, because physical shortcomings can be noticed that are not otherwise documented in the data.


As with any new tool, it is important to understand how that tool works and what the limitations may be. The goal is certainly to develop an accurate automated assessment model. Accurate automated estimation would open endless possibilities. and it really sounds amazing, maybe a little too incredible to be true.


As a general rule, the valuer should be aware that any analytical tool is reliable only as far as the quality of the data entered into it and the analytical model used.


As software evolves and algorithms improve, real estate professionals will increasingly rely on them. The advantages of these solutions are already obvious and allow the process of analysis and evaluation to be simplified. The existing problem of lack of physical visit to the property and lack of qualitative data on the property could be overcome by involving an appraiser in the process but this would not mean that the process is fully automated and left to technology.


AVM estimates contain errors for several reasons, mainly due to problems with the model and data, most often related to the following shortcomings: the model is not adequate, the market is not static, and the AVM model cannot keep up with changed conditions, data quality and volume is unsatisfactory, lack of physical inspections, outdated data.

Financial Analysis

Artificial intelligence

The non-transparent nature of real estate data, especially commercial real estate data, was considered a challenge for automated valuation models, which work best with broad and detailed databases. However, the change in the speed and sophistication of tools based on artificial intelligence, capable of working on these challenges, together with the increase in the number of public databases, has changed the game.


Artificial intelligence could improve the efficiency and accuracy of an automated estimation model that almost always relies on standard hedonistic regression.


What is the advantage of using artificial intelligence in real estate analysis and assessment?


Artificial intelligence can collect data from multiple public databases and records and scan millions of documents in seconds and can even collect data that are not part of centralized public databases. Also, a system that learns from available data has the ability to predict future trends.


Whichever tool is used in the analysis, the quality of the data determines how reliable the result will be and how much weight should be given to the result in decision making.

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