Free Essay

House Pricing

In: Business and Management

Submitted By jfo17
Words 425
Pages 2
Problema: Um detentor de um imóvel muitas vezes não sabe qual o valor que deve pedir quando pretende vender o mesmo e igualmente aquele que deseja comprar e não sabe quanto há-de pagar pelo imóvel que pretende comprar. Neste relatório pretendemos apresentar um modelo estatístico que estime o preço de uma moradia para venda de acordo com as suas mais variadas características.
Metodologia: Primeiro, recolhemos uma amostra de 50 moradias no concelho de Lisboa sabendo que neste concelho existiria certamente um grande número de imóveis à venda. Optámos por olhar tanto para as moradias usadas e para as novas e usando depois uma variável dummy no modelo para diferenciar esta característica.
Base de Dados: Recolhemos a nossa amostra de um só site (www.lardocelar.com) para evitar anúncios que possam aparecer duas vezes em mais que um site. Para garantir a aleatoriadade da amostra, escolhemos moradias de várias páginas de resultados e não somente as que apareciam nas primeiras páginas (por exemplo, uma da página de resultados 200, outra da 321, etc).
Usando o método dos mínimos quadrados e mais precisamente o Excel e a função de Data Analysis construimos o seguinte modelo:
PreçoCasai=β0+β1tipologiai+β2nquartosi+β3ncasasbanhoi+β4areautili+β5areabrutai+β6npisosi+γ1usadoi
Em que tipologiai é uma variável que se refere à tipologia de cada moradia; nquartosi refere-se ao número de quartos que cada moradia tem; ncasasbanhoi refere-se ao numero de casas de banho de cada moradia; areautili refere-se à area útil que cada moradia detém; areabrutai é a área bruta de cada moradia e usadoi é uma variável dummy que toma o valor de 1 quando a moradia é usada e de 0 quando a moradia é nova. Temos que ter error term?
Resultados de Estimação:
PreçoCasai=-267,141+48,959 tipologiai-103,453 nquartosi+13,218 ncasasbanhoi+3,636 areautili+1,510 areabrutai+112,657 npisosi-392,312 usadoi
VER: Intervalo de confiança para o declive de B1, B2....
Testes de hipótese para significância individual de cada variável
Ver o R quadrado e o R quadrado ajustado
Fazer uma previsão
Multicolinariedade
Efeitos interacção

Simulação:
Conclusões: O nosso modelo poderia ser aumentado para incluir outras variáveis determinantes tais como se a moradia tem garagem ou não ou qual o número de lugares de garagem que tem pois isto pode aumentar o preço de uma casa. Poderia igualmente incluir uma variável que abordasse a questão da proximidade da moradia do centro de Lisboa pois crê-se que uma casa mais perto do centro da cidade é relativamente mais cara.
Anexo:

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