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Abstrak :
ABSTRAK
Melihat pertumbuhan penduduk yang cepat dewasa ini masalah perumahan dirasakan semakin penting. Hal ini dapat dilihat dari perbandingan antara jumlah penduduk dengan jumlah perumahan sangat berbeda, oleh karena itu harus mendapat perhatian dari pemerintah. Salah satu perhatian pemerintah adalah membangun rumah-rumah negara untuk tempat tinggal pegawai negeri sipil bersama dengan keluarganya. Selanjutnya, titik berat masalah terletak pada proses pengalihan rumah negara kepada pegawai negeri sipil dengan cara sewa bell. Pokok permasalahan adalah mengapa proses pengalihan rumah negara harus dengan sewa beli tidak dengan pengikatan jual beli?, dan bagaimana pelaksanaan pengalihan rumah negara apabila menggunakan sewa beli?. Dalam penelitian ini digunakan penelitian kepustakaan yang bersifat yuridis normatif, memakai studi dokumen atau studi kepustakaan sebagai alat pengumpulan data, sedangkan studi dokumen tersebut menggunakan bahan hukum primer dan sekunder. Lalu setelah dilakukan analisa, kesimpulam dalam penelitian adalah bahwa berdasarkan Pasal 18 Peraturan Pemerintah Nomor 40 Tahun 1994 Tentang Rumah Negara, pengalihan rumah negara kepada pegawai negeri sipil dilakukan dengan cara sewa beli, sedangkan rumah negara yang dapat dialihkan adalah rumah negara golongan III saja, dan syarat-syarat dari pengalihan rumah negara dapat dilihat dari subjek dan objek perjanjian sewa beli rumah negara yang mempunyai kekhususan. Saran dalam penulisan ini adalah, sewa beli dalam masyarakat Indonesia lebih baik diatur dalam suatu undang-undang, karena dengan adanya undang-undang sewa bell, maka terdapat suatu pegangan yang pasti bagi semua pihak guna menghindari kesimpang siuran, dalam merumuskan isi perjanjian sewa beli walaupun pembelinya pegawai negeri sipil harus tetap perlu dipertimbangkan, supaya tidak menimbulkan kerugian yang berlebihan dipihak penyewa beli dan penyeleksian calon pembeli rumah negara hendaknya dilakukan dengan teliti oleh pihak yang berwenang dan memprioritaskan kepada mereka yang belum mempunyai rumah.

Abstrak :
Bank perlu mengelola risiko kredit yang terkandung dalam portfolio kredit. Salah satu bentuk penilaian risiko kredit dengan menggunakan pendekatan atas dasar numerik adalah credit scoring. Credit scoring merupakan suatu model statistik yang dibangun atas dasar data historis debitur dalam periode waktu tertentu untuk memprediksi probabilitas talon debitur akan default (gagaI bayar) dan umumnya digunakan dalam proses pemutusan kredit. Metode penyaringan calon debitur dengan menggunakan scoring cocok digunakan untuk penyaringan calon debitur konsumtif dengan berbagai keuntungan yaitu mengurangi subyektifitas keputusan kredit, waktu anaiisa lebih cepat sehingga cocok digunakan untuk kredit konsumtif yang jumlahnya massal, bersifat terukur, terdokumentasi dan mudah dikembangkan. Tujuan dari penulisan ini untuk memberi gambaran mengenai pembentukan credit scoring yang dikembangkan oleh Bank X khususnya credit scoring pada kredit kepemilikan rumah (KPR) dan menyusun credit scoring model yang baru sebagai masukan bagi Bank X dalam mengantisipasi perubahan data demografi dan perilaku nasabah kredit konsumtif Bank X sejak implementasi awal model scoring KPR tersebut di tahun 2003. Penulisan difokuskan pada credit scoring model untuk KPR Bank X karena jenis kredit ini mencapai 42 % dari portfolio kredit konsumtif Bank X dan menunjukkan pertumbuhan yang baik. Dari 1908 data debitur KPR Bank X yang berumur 2 - 3 tahun diolah dengan menggunakan logistic regression dan diperoleh model credit scoring baru dengan sebelas variabei yang signifikan terhadap credit scoring model produk KPR Bank X yaitu variabel region, usia, jumlah anak, pendidikan, pekerjaan, tipe perusahaan, income, Debt Service Ratio, Debt to Income, dan Lama Hubungan dengan Bank. Uji terhadap model credit scoring yang dibentuk memberi hasil yaitu nilai Hit Ratio sebesar 82.1 %, yang berarti model dapat mengklasifikasikan sampel dengan correct classification sebesar 82,1 % ke dalam kelompok Good dan Bad , uji Hosmer & f emeshow Significance menghasilkan nilai chi-square 4.532 dengan signifikansi sebesar 0.806 menunjukkan bahwa data prediksi pada model dapat menggambarkan data aktual dan uji KS Statistic menghasilkan nilai sebesar 37.46 % menunjukkan bahwa model ini cukup peka dalam mernbedakan populasi Bad dan Good. Uji terhadap probability of default atas dasar bad rate dari model credit scoring ini menunjukkan bahwa semakin tinggi score yang diperoleh maka probability of default akan semakin kecil. Intepretasi terhadap persamaan credit scoring model mengaitkan tipe talon debitur baik dari data demografi maupun data keuangan dengan nilai bobot skor yang dihasilkan. Pembentukan model credit scoring juga akan memberikan suatu prediksi level acceptance rate kredit dan dengan menetapkan cut off score tertentu maka Bank X dapat memprediksi acceptance rate yang akan diperoleh yang dapat disesuaikan dengan target pencapaian kredit yang sesuai kebijakan ekspansi kredit Bank X. Bila berdasarkan penurunan had rate terbesar, maka cut off score dapat ditetapkan pada selang score 0-20 dengan acceptance rate 81.34 -91.72 %.

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Credit portfolio risk management is a main issue that bank should address and per formed well. One of the numerical approach measurement of risk that can be used by bank is credit scoring. Credit scoring is a statistic model that is built based on debitor 's historical database in certain period q f time. This model is constructed to predict the probability of debitor's default, and at the moment, widely used by banks in the credit approval decision making process. Scoring screening method is appropriately used to sorting out consumer debitor applicant due to its advantages in reducing credit approval subjectivity bias and shot-tens analysis time. Accordingly, scoring method is suitable to be used for analyzing mass consumer credit that is measurable, document able and easy to develop. The purpose of this paper is to exemplify credit scoring construction developed by bank X; in particular credit scoring on housing loan (Kredit Kepemilikan Rupiah), and composing a new credit scoring model proposition for bank X in order to anticipate changes in demography data and consumer credit's client attitude since its initial KPR credit scoring model implementation in 2003. This paper is focused on bank X's KPR credii scoring model as this type of credit comprise up to 42% of bank X consumer credit portfolio, and showing a constant and promising growth . About 1908 data of 2-3 years Bank X's KPR debitor clients is processed using logistic regression, and a new credit scoring model with ten significant variables has been acquired. namely variable of region, age, number of children, level of education, occupation, type of company, income, Debt Service Ratio, Debt to Income and relationship period with bank. The analysis of this new credit scoring model resulted a 82.1% Hit Ratio, which mean that this model is capable to classify sample into Good and Bad group up to 82.1% of the correct classification. Thus, a 4.532 chi square value of Hosmer & Lemeshow Significance analysis with a 0.806 level of significance implies that model 's data prediction can illustrate an actual and tangible data. Furthermore, the 37.46 % value of KS Statistic analysis exhibits model 's high level sensitivity in differentiating Good and Bad population. Analysis of probability of default of this credit scoring model, using bad rate based test, shows. that the higher the score is the lower probability of default will be. Moreover, credit scoring model equation interpretgtiott links debitor applicant's demography and financial type data with the weighted scored that it produced. The credit scoring model formation will also provide prediction of credit acceptance rate level and accordingly, with a certain cut off score level, bank X can also predict the adjustment of expected acceptance rate with bank X's targeted loan expansion policy. In the condition based on the highest bad rate decreased, cut off score then, could be pegged at the 0-20 interval with acceptance rate 81.3-1 -91.72 % .

Abstrak :
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