Interpretation and measurement of the spread between asking and selling prices in the Italian residential market

The lack of transparency in the property market and the resulting difficulty in finding comparables to use in property valuations, very often forces evaluators to substitute the asking prices with the selling prices in the market approach. This alternative is now also accepted by case law but has the limitation of having to quantify, albeit very roughly, the correction to be made in relation to the probable spread between the asking prices, taken as a reference, and future selling prices. The importance of the asking prices to understand the market, is acknowledged in international literature which has mainly focused (starting from the analysis of the prices themselves and the time spent on the market), on the search for the best sales strategies, or on the measurement of the illiquidity of the property market. This study, in an innovative way, also on the basis of the relationships already proven, instead, attempts to interpret and measure the difference between asking and selling prices, in order to build a reference for the adjustments made to the former in estimation practice. The target is pursued through the construction of a multivariate analysis model on a sample taken over a 12- year interval in the city of Potenza, Italy. The analysis allowed to measure and interpret the marginal contribution that macro and microeconomic variables provide to the explanation of the spread under investigation. La mancanza di trasparenza nel mercato immobiliare e la conseguente difficoltà di rilevare utili comparabili da utilizzare nelle stime immobiliari costringe i periti, molto spesso, a sostituire nel procedimento diretto i prezzi di vendita con i prezzi richiesti. Si tratta di una alternativa ormai riconosciuta anche dalla giurisprudenza, ma che ha in sé il limite di dover quantificare seppur in modo molto approssimativo la correzione da apportare in relazione al probabile spread tra i prezzi richiesti, presi a riferimento, e i futuri prezzi di vendita. L’importanza dei prezzi richiesti per l’interpretazione del mercato è riconosciuta nella letteratura internazionale la quale si è prevalentemente concentrata, partendo dal- l’analisi di questi e del tempo sul mercato, sulla ricerca delle migliori strategie di vendita, o sulla misura della il- liquidità del mercato immobiliare. Questo lavoro, in modo originale, anche sulla base delle relazioni già di- mostrate, prova invece a interpretare e misurare la differenza tra prezzi richiesti e prezzi di vendita, al fine di costruire un riferimento per la correzione da apportare ai primi nella pratica estimativa. L’obiettivo è perseguito mediante la costruzione un modello di analisi multivariata su un campione rilevato su un intervallo di 12 anni nella città di Potenza. L’analisi ha consentito di misurare ed interpretare il contributo marginale che variabili macro e microeconomiche forniscono alla spiegazione dello spread indagato.


INTRODUCTION
The estimation of the market value of a real estate property, which can meet different needs, must always be developed through market analysis and comparison.In the market approach a comparison is made between the property to be estimated and the real estate units (socalled comparables) which are similar in terms of intrinsic and extrinsic characteristics, and for which the price of a recent sale is known.This requires market transparency and the possibility of drawing quickly and easily on a database that stores such information.
The Greti index (Global real estate transparency index), published in 2020 by Jones Lang Lasalle Incorporated (JLL), which has been monitoring transparency in the real estate The lack of transparency in the property market and the resulting difficulty in finding comparables to use in property valuations, very often forces evaluators to substitute the asking prices with the selling prices in the market approach.This alternative is now also accepted by case law but has the limitation of having to quantify, albeit very roughly, the correction to be made in relation to the probable spread between the asking prices, taken as a reference, and future selling prices.The importance of the asking prices to understand the market, is acknowledged in international literature which has mainly focused (starting from the analysis of the prices themselves and the time spent on the market), on the search for the best sales strategies, or on the measurement of the illiquidity of the property market.This study, in an innovative way, also on the basis of the relationships already proven, instead, attempts to interpret and measure the difference between asking and selling prices, in order to build a reference for the adjustments made to the former in estimation practice.The target is pursued through the construction of a multivariate analysis model on a sample taken over a 12year interval in the city of Potenza, Italy.The analysis allowed to measure and interpret the marginal contribution that macro and microeconomic variables provide to the explanation of the spread under investigation.
behaviour of the broker.This study aims at investigating the effects that micro and macroeconomic factors can exert on the spread between the asking price and the selling price.This objective, while on the one hand, may constitute argumentative material for a more in-depth examination of the problem, on the other, it makes the essential methodological principles of an estimation in quantitative terms both of the spread and consequently of the market value, unambiguous.

LITERATURE REVIEW
In There are few works in the literature that, like this one, focus on the spread between the asking price and the selling price, and attempt to examine its possible causes.Conversely, many authors have studied the relationship between selling price and time on the market (TOM).
The objectives between what this study proposes and the research that has already been developed on the subject are also different.On the one hand, this study attempts to offer real estate appraisers the possibility to correct their asking prices to make them usable comparables, on the other hand, the literature has so far predominantly focused on spread analysis aimed at finding the best sales strategies, or measuring the illiquidity of the real estate market.As a matter of fact, illiquidity is an intrinsic feature of the real estate market.The relationship between TOM and price dynamics constitutes an indicator of real estate illiquidity (Cirman et al., 2015;Jud et al., 1995).Although the objectives of this study are different, the analysis of the literature is useful because many authors have shown, as can be imagined, that there is a close relationship between the time on the market and the spread between the asked (or listed) price and the final (or contracted) price on the market (Jud, et al., 1995).In particular, the higher the list price compared to the sale price, the longer the property will remain on the market (Lazear, 1986;Anglin et al., 2003;Ferreira E.J., Sirmans G.S 1989).Therefore, the analysis of the literature on the factors that determine a higher TOM can help in the selection of the variables that can condition the difference between the asking and selling price.In reality, a fundamental determinant of the aforementioned difference is precisely the strategic behaviour of the broker who, in choosing the list price, logically assesses the macro-and microeconomic boundary conditions, knowing full well that an incorrect definition of the initial price is costly both in terms of selling time and the final price (Knight et al., 2002).The duration of a property on the market is therefore influenced by the skills of the brokers.Most brokers try to portray themselves as having special skills and knowledge that enable them to sell a residence faster and at a higher price than their rivals.These elements, in turn, influence the seller in the choice of the broker as the commission structure has no impact on the sales price (Rutherford and Yavas, 2012).When a house is put on the sector since 1999, by analysing 99 countries and territories and 163 cities around the world, placed the United Kingdom, the United States and Australia on the podium, and assigned Italy 17th place (up from previous performances).However, the discreet result obtained by Italy is misleading if referred to price transparency.The Report is based on 210 indicators divided into six areas: governance of listed companies, sales processes, legislation, availability of market data, sustainability and performance indicators.Unfortunately, in Italy the actual selling price still remains well protected in the bureaucratic tangles of the Property Registry.The desired Real Estate Publicity is disregarded due to an outdated access to the records and costly investigation.But even if a notary deed were available, in some segments of the real estate market the price declared in the deed would most likely not correspond to the actual price.This is due to a complex tax law that only in certain cases forces contractors to declare the price paid in the deed.Therefore, on the one hand the difficulty of accessing databases, and on the other the possibility that the price recorded is not the actual one, often forces those called upon to make estimates to use asking prices instead of selling prices.This method of constructing or integrating the comparison sample is now common in estimation and also to some extent legitimised both by case law (Civil Cass.Ord.Sec. 1 Num.20307 of 31/07/2018) and by the income revenue authority (27 July 2007 «Provisions on the subject of identifying the criteria useful for determining the normal value of buildings, as per art. 1, paragraph 307, of Law no.296 of 27 December 2006»).However, these asking prices generally deviate to a greater or lesser extent from the selling price.Therefore, the use of the asking price in the valuation process requires the ascertainment of their likely spreads from the actual selling prices.This operation cannot disregard a preliminary, in-depth knowledge of the phenomenon, which must lead to results that are logically differentiated according to the trends in the market, the intrinsic and extrinsic characteristics of the asset and the behaviour of the brokers in its various segments.The extent of the spread can depend on many factors.Some of which can be traced back to the determining factors of the economic conditions, which may therefore affect the national or solely the local property market.These macroeconomic factors, by altering the balance between supply and demand for real estate, cause fluctuations in the «average» amount of the spread between the asking and selling price, especially in the preand post-transition phases.The microeconomic factors are to be identified among the extrinsic characteristics, essentially attributable to the location, and the intrinsic characteristics of the property.Besides these, there is an element which is largely linked to them but which, more than any other, can condition the differential between the final price and the asking price.This is the strategic Interpretation and measurement of the spread between asking and selling prices in the Italian residential market journal valori e valutazioni No. 31 -2022 7 market, the seller has to choose the initial list price, knowing that potential buyers will draw information from the list prices to define the price range in which to look for properties of interest.Setting an initial price too high or too low affects the marketability of the property.The initial asking price clearly plays a critical role in the transaction, and acts as a reference for potential buyers (Glower et al., 1998).An initial price that is too high could discourage potential buyers by extending the time on the market.However, some authors have shown that while a higher list price makes the process whereby demand tends towards the asking price less rapid, it also increases the likelihood of higher bid prices (Lippman and McCall, 1986;Haurin. et al., 2010).Conversely, low asking prices correspond to more potential buyers, thus a quicker sale, lower transaction costs but also a lower probability of high transaction prices (Anglin et al.,2003;Arnold, 1999;Yavas and Yang,1995;Deng et al. 2012;Horowitz, 1992;Knight et al.,1994).
The seller is usually faced with the two conflicting objectives of maximising the transaction price and minimising the selling time (Trippi, 1977;Miller, 1978).
In real estate markets, a trade-off occurs between time on the market and selling price, and the 'pricing strategy' is the balancing act between the two (Liu, 2021).For example, the results of the empirical analysis (Beracha and Seiler, 2014) suggest that sellers employing the 'just below' pricing strategy are able to set a higher list price for their property than other pricing strategies.However, while buyers are more attracted to properties priced 'just below', they also tend to negotiate the price further.However, the effect of the 'just below' strategy produces a final transaction price that is higher than when using the two alternative strategies (exact price and round price).Sometimes, the seller's character can change the relationship between TOM and final price.Genesove and Mayer (1994) show that owners with high loan-to-value ratios take longer to sell their properties than owners with low loan-to-value ratios.Properties with high loan-tovalue ratios are offered at higher prices and consequently, when sold, receive higher prices than those units with less debt.The owner with large debts sets a higher asking price than unencumbered owners, accepting a lower probability of sale in exchange for a higher final sales price.
The sales strategy can also be influenced by micro-and macroeconomic factors that affect the TOM and thus the difference between the asking and selling price.
Assuming the behaviour of sellers is homogeneous, the variance of the TOM and thus of the spread should be linked directly to them.Macroeconomic factors include those that could condition the real estate cycle (Glower et al., 1998;Yavas and Yang, 1995;Anglin et al., 2003;Cirman et al., 2015;McGreal et al., 2016) or simply represent its trend.As a matter of fact, some studies relate the TOM to the Residential Property Price Index (Hui and Yu, 2012).
In down markets or normal economic times, the list price generally exceeds the sale price, however when the real estate market grows, residences can also be sold at prices above the list price (Haurin et al., 2013).The determining factors of the real estate cycle are undoubtedly: disposable income (Sirmans et al., 2010), mortgage rates (Leung et al., 2002;Sirmans et al., 2010;Kang et al., 1989;Ferreira and Sirmans, 1989) or the unemployment rate (Hui and Yu, 2012).
The literature has also shown that the marketability of a residential property and thus TOM and spread can depend on the geographical area in which the property is located (Curto et al. 2012), and on its quality characteristics (Ong and Koh, 2000;Hui and Yu, 2012).Kang and Gardner (1989) show that the TOM is significantly shorter for newer houses, especially those in the middle or high price range, while the size of the house has no significant effect.On the basis of the indications provided by the literature, we constructed the model presented in this paper, which tends to measure how intrinsic and extrinsic variables of the residential property can affect the differential between asking and selling price.
In order to eliminate the effect of the element that the literature considers to be one of the major determining factors of the TOM and thus on the differential between asking and selling price, the sample was constructed on data from a single estate agency.

THE CASE STUDY
The analysis is carried out on a sample consisting of 202 residential properties located in the city of Potenza, that were bought and sold between 2010 and 2021 (12 years).The data were collected by a single real estate agency in the city of Potenza.This made it possible to purify the data from the effects deriving from the broker's particular sales strategy.In addition, the extent of the temporal interval in which the data fall made it possible to capture the effects of macroeconomic factors on the spread under investigation.
This is the first study of its kind carried out in Italy, given the objective difficulty of acquiring the data, which are normally jealously guarded by real estate brokers.
The information acquired from the broker was translated into the explained variable (the spread between the asking and selling price) and into other microeconomic variables (extrinsic and intrinsic characteristics) of the property, each of which was expressed with an appropriate measurement system.The choice of the model's explanatory variables was therefore conditioned by the information available from the real estate agency.The macroeconomic variables whose measurement was acquired from national databases (Real Estate Market Observatory and Bank of Italy) were selected on the basis of the cited literature and analyzed in the previous section.
The variables considered and the corresponding methods of measurement are described below.
• Spread [ ], difference between selling price and asking price, expressed as a percentage [%].
• Construction Year [CY], a variable measured in number of retrospective years starting from the year 2021.
• Saleable Floor Area [SFA], is defined as the floor area exclusively allocated to a residential unit including balconies, verandahs, utility platforms and other similar features but excluding common areas.
• Preservation and Maintenance [PM], state of preserva-tion and maintenance, expressed on the following scale: 1 = to be renovated, 3 = habitable, 5 = renovated/new.Note: DI, S, I and MR are variables surveyed quarterly by the Bank of Italy, the value used is the average annual value expressed in euros.
Table 1 shows the minimum, maximum, mean, and standard deviation for each variable.
The spread has a mean of 16.86%, a minimum value of 2.86% and a maximum value of 42.86% with a standard deviation of 8.4% Table 2 shows the Pearson correlation measure between the variables.The statistical significance of the correlations is congruent with what was deductively expected about the causal links between the spread and the explanatory variables.
Table 2 shows that the main factors correlated with the difference between the asking and selling price, in order of decreasing importance with significance of correlation at 0.

THE MODEL
A stepwise multiple linear regression model was chosen for the data analysis.Stepwise regression is used in cases where it is necessary to select an «optimal» sub-set of variables from among the possible ones.In the case under consideration, given the strong correlation between the explanatory variables, and therefore in order to avoid multicollinearity problems, the stepwise model is the most suitable.
The procedure starts with the assumption that there are no regressors in the model other than the intercept.
Here below, one variable at a time is added to the model.A significance value of the FRatio (FOUT) variable is set.It indicates the ratio between the variance explained by the model and the residual variance, below which the contribution of further introduced variables is not considered important.
The variable with the highest significance is selected first, i.e. the one for which the FRatio value for the simple linear regression is the highest, or rather, the one with the  indicates that as the price of the property and the age of the building increase, as the maintenance conditions improve, and as one moves away from the urban centre, the differential between the asking and selling price decreases; likewise, the spread decreases as the savings rate, the mortgage rate, and the price values increase (expanding real estate cycle).
Observing the standardised coefficients, the model shows that the greatest weight in explaining the variance of the spread is the sale price, then, in descending order, the year of construction, savings, location, interest rate on mortgages, the dynamics of the property cycle and maintenance conditions.Observing the coefficients of the regression, it can be concluded that for average increases of about 10,000 euros in price, the spread was reduced generally by about one percentage point.
Buildings with an age difference of 10 years show a difference in the spread of about 3 points.A change between 5 and 6% in savings resulted in a change of about one percentage point in the spread.There is a difference of about 9 percentage points in the spread between the most central and the most peripheral areas.with r are the degrees of freedom and aj the added variable.
At each step, all the regressors previously considered in the model are tested again through the evaluation of the relative F.
• FRatio(j) < FOUT: the variable is removed, although it was previously included.• FRatio(j)> FOUT: the regressor variable is not excluded from the model.
A previously introduced variable may in fact be redundant due to the introduction of new variables.
Once the final model is defined and deemed statistically significant, hypothesis tests are conducted on the individual parameters to determine whether each independent variable offers a significant contribution to the model.

THE RESULTS
The processing after 9 steps produces a final model with 7 variables out of 12.The excluded variables are DI, Save, NTN and HPI.The NTN variable introduced in step 2 was then excluded in the second last step (8).The inserted variables are respectively, SP, S, CY, OMIz, PM, T, MR.Table 3 shows the results.The ANOVA test shows that in the 9 steps, the variables introduced improve the interpretation of the phenomenon, the F test is always significant.Table 4 indicates the coefficients of the variables and the significance (t student) for the last three steps.The Durbin-Watson statistic shows that there is no autocorrelation between the residuals of the model, which is essential given that these are observations over time (12 years).About 58% of the variability of the response is explained by the variables introduced.
Observing the dispersion graph of the standardised residual (Fig. 2), the hypothesis of homoscedasticity is not violated.The hypothesis of a normal distribution of errors is also verified (Fig. 3).For all variables, the observed pvalue is lower than the theoretical p-value (< 0.05), each explaining a significant proportion of the variance of the spread.
The regression coefficients are all negative.The model

CONCLUSIONS
The Italian real estate market has always been characterised by a lack of transparency in transactions, although regulations have been enacted in recent years that attempt to reduce this opacity.The lack of transparency limits the possibility of constructing statistically significant samples of sales prices that can be used in estimates.In the absence of reliable sources of market prices, easily available asking prices often become the reference for those making property estimates.The use of these data as comparables, however, has the limitation of the prospective discount in the transaction from the asking price.
The proposed model provides an explanation for the variability of the spread between the asking and selling price by identifying and selecting the main micro-and macroeconomic variables on which it could depend.The results show that the spread decreases as the price increases, and also as the maintenance conditions or the quality of the physical and technical characteristics of the property increase.This relationship is explained by the strategic behaviour of the brokerage agency which balances time on the market and list price.More valuable properties (in relation to their larger size or even better maintenance and use) are placed in a market segment with a more rarefied demand.The greater difficulty in selling and the need to not extend the time on the market too much with the risk of losing the mandate from the seller, pushes the agency to accept a sales proposal very close to the expectations of the demand (bid price).This therefore justifies a smaller final spread.Looking again at the microeconomic characteristics, the lower spread for more peripheral and older buildings is justified by the fact that, in the period covered by the study, this segment was the focus of the highest demand for residential property, consisting of young couples or households with a lower affordability index, intended as the degree of accessibility to the purchase of a house.The higher demand justifies lower discounts on the asking price.Higher demand is also at the basis of the lower spread resulting from a higher savings rate, which implies greater affordability for the purchase.But an increase in demand in turn produces an increase in average prices, which justifies the same relationship between spread and the measure of variability in the housing cycle.In considering the possible determinants of demand, the inverse relationship between mortgage interest and spread seems instead to contradict the easy deduction that demand is related to an inverse relationship to mortgage rates.
However, it should be specified that in the period taken into consideration, the fiscal expansion policy (with progressive reduction of interest rates to which interest on mortgages is closely linked) tried to stem the economic crisis and a trend towards deflation.In the particular temporal and geographical context, the inertia of the crisis and the not-so-rosy expectations of consumers are reflected in the correlations between some macroeconomic variables.A steady fall in interest rates has been matched by an increase in savings and a reduction in investments by consumer households.But dropping interest rates have instead stimulated business investment.This has led to an increase in supply, which has resulted in an almost perfect direct correlation between interest rate changes and house prices.The increased supply in view of a buying resistance, has also produced an increase in the spread.
Measuring the contribution these variables offer towards an explanation of the spread, however, is limited by the specificity of the context under investigation.In order to construct a valid reference to the use of asking prices as a proxy for sales prices in real estate estimation, the proposed statistical approach will have to be validated by investigating other cities, different segments, different periods and by using different models.
La strategia di vendita può anche essere condizionata da fattori micro e macroeconomici che agiscono sul TOM e sulla differenza tra prezzo richiesto e prezzo finale di vendita.

Table 1 -
Variables statistics

Table 2 -
Pearson correlation measure between the variables **. correlation is significant at the 0.01 level *. correlation is significant at the 0.05 level

Table 4 -
Coefficients and significance of the explanatory variables No. 31 -2022 CIRMAN A., PAHOR M., VERBIC M., Determinants of Time on the Market in a Thin Real Estate Market, Economics of premiums, and days on the market, Journal of Real Estate Finance and Economics 2(3), 1989, pp.209-222.GENESOVE D., MAYE, C.J., Equity and Time to Sale in the Real RPPI e NTN in tutta Italia e nella città di Potenza dal 2004 al 2020 (fonte Agenzia delle Entrate -Osservatorio del Mercato Immobiliare).