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随着社会经济水平和社会文化的发展，居民对于良好生态环境和生活品质的需求愈加强烈，正是这种对住宅周边良好生态环境的偏好，各类观景房如雨后春笋般层出不穷，进而导致观景房的价格过于虚高。观景房的价值主要是优质景观资源所带来的价值，但城市生态景观的价值难以准确衡量且现实中对于观景房的评估普遍选择传统的市场法。传统的市场比较法在实际的观景房价值评估过程中，常常出现估价案例选取相对随意以及因素修正过于主观的问题，再加上未结合观景房的特点，不能充分考虑对观景房价格特有的影响进而导致评估结果不客观。因此，通过对市场比较法的优化，一方面可以使市场比较法的估价过程更加科学与合理，另一方面能够丰富观景房价值评估的内容，为观景房经济价值的判断提供参考。

在本文中，经过对大量文献的分析研究，选择了两个影响海景房价值的主要因素：海景可视性和距海边的直线距离。为了解决市场比较法存在的问题，本文在案例选择流程中导入了灰色关联分析模型，通过统计可比案例与评估对象相互之间的关联性来评估相似性，并按照关联度大小来选择可比案例。在因素调整流程中，本文引入了层次分析法，通过构建一个梯级结构，测算出各种因素在房地产价格中的权重，并根据这些权重来评估海景房的市场价值，可以帮助我们更好地理解房地产价格的影响因素，进而更好地评估景观对房价的影响。在“烟台市金海岸花园”评估项目中，本文使用改进的市场法来构建这个模型，并测算出待估对象的市场价值。研究发现：①灰色关联分析可以有效提升市场法中可比交易案例的选择效率；②层次分析法可以有效优化因素修正步骤中的主观问题；③采用灰色关联分析和层次分析法相结合改进的市场法，可以更加准确地评估海景房市场价值，并且得出的评估结果与实际交易价格更加接近。因此，引进灰色关联分析和层次分析法，可以有效提高海景房市场价值的评估效率，使得评估流程更加规范、更加合理，从而更好地满足市场需求。

With the development of social economy and culture, residents need more and more good ecological environment and quality of life. It is precisely this preference for a good ecological environment around the residence that various types of viewing houses are springing up one after another,  resulting in the price of viewing houses being too high. The value of landscape house is mainly the value of high-quality landscape resources, but the value of urban ecological landscape is difficult to accurately measure and in reality, the traditional market method is generally chosen for the evaluation of viewing rooms. The traditional market comparison method in the actual evaluation process of the value of the observation house often has the problems of relatively random selection of evaluation cases and too subjective factor correction. In addition, it does not combine the characteristics of the observation house, and cannot fully consider the unique impact on the price of the observation house, resulting in the evaluation result is not objective. Therefore, through the optimization of the market comparison method, on the one hand, it can make the valuation process of the market comparison method more scientific and reasonable, on the other hand, it can enrich the content of the evaluation of the value of the observation room, and provide a reference for the judgment of the economic value of the observation room.

In this article, after analyzing a large number of documents, two main factors that affect the value of seascape houses are selected: seascape visibility and the linear distance from the seaside. In order to solve the problems existing in the market comparison method, this article introduces a grey correlation analysis model in the case selection process, evaluates the similarity by statistical correlation between comparable cases and evaluation objects, and selects comparable cases based on the degree of correlation. In the factor adjustment process, this article introduces the Analytic Hierarchy Process (AHP), which calculates the weight of various factors in the real estate price by constructing a cascade structure, and evaluates the market value of seascape houses based on these weights. It can help us better understand the influencing factors of real estate prices, and then better evaluate the impact on house prices. In the evaluation project of "Yantai Golden Coast Garden", this article uses an improved market approach to construct this model and calculate the market value of the object to be evaluated. The research finds that: ①Grey correlation analysis can effectively improve the selection efficiency of comparable transaction cases in market law; ②Analytic Hierarchy Process (AHP) can effectively optimize subjective issues in factor correction steps; ③Using the market method of gray correlation analysis and analytic hierarchy process can more accurately evaluate the market value of seascape houses, and the evaluation results obtained are closer to the actual transaction price. Therefore, the introduction of grey correlation analysis and analytic hierarchy process can effectively improve the evaluation efficiency of the market value of seascape housing, make the evaluation process more standardized and reasonable, and better meet market demand.