The results of MUSSA allows to model and analyze the great urban systems and to evaluate specific projects, from a social and private perspective

• Demographic and economic growth,
• Application of urban management policies,
• Execution of transport projects,
• Execution of real estate projects and
• A change in the structure of behavior of the real estate demand.

MUSSA model, like the software that implemented it, has been designed to forecast the Urban Real Estate Market, from the simulation of the demand and the supply behavior of this market underbefore different verse conditions; those acting directly or indirectly, as on both functions, like restrictions or incentives.

Evidently, from with MUSSA thea user will be able to obtain information from the variables that characterize this market and that are necessary for the analysis of other urban systems. The most important variables are the following ones:

a) Real Estate Supply

• N° of occupied real state according to the type of real estate and zone
• Occupied land and construction areas according to the type of real estate and zone

b) Location

• Nr of consumers, bysocioeconomic categories, located at each types of real estate and zone
• Land and construction areas occupied by each category of consumer in the different types of real estate and zone
• Monthly average income of the residents by zone

c) Real Estate Rents

• Real estate rents according to the type of real estate and zone.

• Analyze the trip generation process and the attraction of trips of the urban transport system.
• Study the environmental system of the city, by the location of fixed sources of contamination in it.
• Evaluate the economic benefit of certain productive activities and services from information of the location of their consumers and competing activities. Define the optimal locations of activities as to maximize profit or social benefit.

On the other hand, the MUSSA user will be able to simulate the impact that exerts a largenumber of scenario variables on the Urban Real Estate Market. An scenario is a set of data, parameters and MUSSA boundaryconditions that the user can specify and that the model uses to carry out a prediction of this market. The user can build different scenariosdue to the great amount and variety of elements that compose those and, in addition, by the fact that they can be modified independently.

With the results that MUSSA provides, the user will be able to carry out an economic evaluation of the impacts caused by the following development of the city:

• Demographic and economic growth,
• Application of urban management policies,,
• Execution of transport projects,,
• Execution of real estate projects and
• A change in the structure of behavior of the real estate demand.

Next these five types of scenarios variables are defined and analyzed..

Economic and Demographic Growth

MUSSA is sensitive to the growth or decrease of the economic activity of the city and to demographic growth and change in the socioeconomic structure of the population. Although MUSSA has built-in models that define all the consumers (householdsand companies) to be located in each forcasting year, the user could introduce his own estimations of these variables.

Application of Urban management policies: regulations, subsidies and taxes in the Urban Real Estate Market

• Maximum feasible area of land for location: it is the maximum area of the land that can be used for the location of activities and corresponds to the area of zonal land, less the area devoted to public use, as roads, landmarks of the nature (rivers, hills) and plazas and public parks.
• Minimum property size: it is the permisted minimum land surface of each real estate.
• Maximum building rate: it is the permited maximum ratio between the built area and land lot area of each real estate
• Maximum high of building: it is the permited maximumhight of buildings..
• Maximum percentage of building occupation: it corresponds to the maximum percentage of the land, that the construction of real estate can occupy
• Land uses allowed: this regulation defines the types of consumer allowed to be located in each zone.
• Limites on residential density and population: This regulation defines the maximum and minimum values of the residential and population density of each zone.

In this context, it is useful to use MUSSA to forescast the behavior of the real estate market under different regulated environmenst, to evaluate if they cause or not the expected effects and how relevant they are in the finalsystem. Other tools for thr urban management available tothe authorities are Location Subsidies and Taxes policies. These instruments correspond interventions in the market which are modelled in MUSSA asxogenously defined amounts that increase or diminish the consumer's bid , which produce an chnage in their location probabilities, which also affects the expected location pattern, land use and rents at equilibrium. These amounts of subsidies and taxes are (des)incentives for the location of activities in the city, so by their introduction as an element of the scenario it is possible to obtain predictions of their impact in the urban development.. There is an wide range of possible types of subsidies or taxes that the user can define, considering that they can vary according to the consumer socioeconomic cluster, the real estate building type and the zone.

There is a number analysis about the impact caused by these tools of urban management. For example, it is possible to ask:

• How do the real estate supply and rents and the location of activities react to changes in these instruments react?
• Who capitalizes the economic (dis)benefits associated to modifications of these instruments?

Execution of a transport project

The microeconomic framework of MUSSA allows to establish a consistent relation between the transport and land use markets, of which rigorous and coherent measures of the final benefits of a project can be extracted. The execution of a transport project will cause changes in the transport system of the city, which are perceived by consumers affecting the demand for location options. This process is modeled in MUSSA by considering that such changes in the transport systems induce changes in access measures of different zones of the city, which are considered like important attributes in the location bid functions of residential and nonresidential consumers of MUSSA. Thus, the execution of a transport project can affect the location of activities and the creation of real estate rents in the city and MUSSA allows simulating this impact.

Observation: if the transport project causes reduces the available area for the location of activities, for example, due to the increase of the road infrastructure, the project also affects the first regulation briefed in APLICATION OF URBAN MANAGEMENT POLICIES above.

Execution of a real estate project

The disaggregated structure of MUSSA allows to represent the influence that exerts on the the Real Estate Market of a city a specific real estate project of urban importance. Thus, it is possible to incorporate to MUSSA information of projects in execution to increase its predictive capacity, but also with the purpose ofobtaining an economic, private and social, evaluation of certain real estate projects portafolio.

Change in the structure of behavior of the real estate demand

The economic theory that sustains MUSSA model proposes the existence of the bid function of each consumer, that assess the consumer's value of each real estate property in the market, which defines the location pattern of activities and the creation of rents in the city. This function corresponds to the price that the consumer is willing to pay for by a specific real estate. In MUSSA, bids are specified asfunctions of real estate attributes that describe both the consumer and the supply characteristics. The set of parameters that define bid functions are associated to the valuation of different attributes specifiedand allow weighting the relative importance of them. Therefore, when the modeler modifies these parameters, he simulates a change in the behaviour of consumers as locators in the market. Therefore, the modeler can represent changes in the perceptions of a certain attribute (or a group of them), modifying the associated parameter , which may be done for each consumer cluster. For example, the parameters associated to attributes that describe the surroundings of a house could be modified, to increase or to diminish the importance of the zonal configurations in the decisions of residential location.