A hedonic model is specified for asking prices for apartments in Donetsk (Ukraine). This model is used to determine statistically significant location attributes. These attributes can be used for land assessment in a city where data on the land market are lacking. Distance gradients for CBD accessibility are investigated in different geographical directions. Separate models are created for sub‐samples located inside and outside the city centre. A spatial weight matrix is used to detect spatial autocorrelation. The regression results are compared with the valuation of experts. Vietos atributų analizė su hedonistiniu modeliu siekiant nustatyti butų kainas Donceke (Ukraina) Santrauka Apibrėžtas hedonistinis modelis, leidžiantis nustatyti butu kainas Donecke (Ukraina). Pagal ši modeli nustatomi statistiškai reikšmingi vietos atributai. Šiuos atributus galima naudoti vertinant sklypus mieste, kur trūksta duomenų apie žemes rinka. Nagrinėjami atstumo gradientai siekiant įvertinti prieiga prie centriniu verslo rajonu įvairiomis geografinėmis kryptimis. Sukurti modeliai bandomiesiems objektams, esantiems miesto centre ir už jo. Remiantis erdves svorine matrica, nustatoma erdves autokoreliacija. Regresijos rezultatai lyginami su ekspertu vertinimais.

PurposeThe purpose of this paper is to extract the location attributes, which are the most important for market value of real estate in countries with well‐developed markets.Design/methodology/approachIn this paper meta‐analysis is applied for extraction of location attributes and the weights of their importance. The outcomes of existing regression models created in different countries mainly with a developed real estate market are used. A total of 81 models described in 39 sources are analysed.FindingsThe paper finds that the lists of statistically significant location attributes, which influence market value, are obtained for different real estate types. The weights of attributes' relative influence are compared, where possible.Research limitations/implicationsIn the paper meta‐analysis is also applied for a limited number of empirical studies. However, for land and residential real estate the number of sources is sufficient to make a substantiated conclusion. The application of the outlined location attributes is a subject for future research.Practical implicationsThe paper shows that the lists of important location attributes can be used for practical specification of the valuation models for urban land and other real estate in countries where the market is underdeveloped, to increase the degree of objectivity and market orientation.Originality/valueThe paper is one of the few studies which synthesize the findings of existing regression models with respect to location attributes generally. The method of weights' estimation is original. The result of the paper has practical value for real estate valuation in countries with an underdeveloped market.

UrbanSim has signi cant data requirements. In particular, it requires disaggregate data (traditionally at the 150 meter by 150 meter gridcell level) for employment, households, and buildings. While such data are not always easily available, most regions have readily available data in a more aggregate form, o en at the level of traffic analysis zone (TAZ) or other municipal divisions. is paper describes two UrbanSim applications for the cities of Brussels, Belgium and Lyon, France that adopted different approaches of using aggregate data. In Brussels, aggregate zonal data were disaggregated to the gridcell level. In the Lyon application, the zone was used as the unit of analysis and as such, each zone corresponds to one gridcell. e objectives of this paper are: 1) establish whether an UrbanSim model can be developed using aggregate data; 2) describe two different approaches to using aggregate data with UrbanSim and evaluate; and 3) evaluate the advantages and disadvantages of using aggregate data, as well as the two different approaches described. In doing so, it advances knowledge in the eld of transportation and land use modeling by helping modelers evaluate the use of an increasingly popular integrated transportation land use modeling option. Several conclusions ow from this work. First, aggregate data can be used to develop UrbanSim models. Second, only a limited amount of disaggregate information can be drawn from aggregate data. In the context of UrbanSim, this is manifested in models with relatively few variables and dubious simulation results-in other words, while it is possible to develop an UrbanSim application with aggregate data, it should not be used for applied analysis. Finally, the development of such models can be a relatively low-cost exercise to gain familiarity with UrbanSim's functioning and data requirements. As a result, it can also be seen as an important rst step to developing or evaluating UrbanSim for application in a new region.

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